Introduction

Over the course of the last 15 to 20 years, statistics and probability have fundamentally changed the world of professional sports. This statistical ‘revolution’ is romanticized in the movie “Moneyball,” based on Michael Lewis’ book of the same name. In the film, set in 2002, the General Manager of the MLB Oakland Athletics, Billy Beane (played by Brad Pitt) teams up with Peter Brand, a fictious Yale economics graduate major that is based on the real life assistant GM at the time, Paul DePodesta (played by Jonah Hill) to radically alter how the Oakland A’s assess player value. In order to compete with MLB teams that have much larger player payrolls, Beane and DePodesta had to find players that were undervalued by the standard collective wisdom of baseballs’ scouts, managers, and coaches. As such, they had to apply a new method of evaluating player value.

They defied commonly followed baseball stats, and the intuition of their scouts, and started to use sabermetrics. Roughly defined, sabermetrics is an empirical analysis of in-game activity. Through advanced statistical analysis, certain indicators (on-base percentage, slugging percentage, etc.) were determined to be better predictors of offensive success than the ‘standard’ stats (batting avg, stolen bases, etc.). Focusing on these stats, Beane and his staff were able to acquire players that were atypical and undervalued. It helped the Oakland A’s to set a 20-game win streak, and successfully compete with franchises with significantly larger payrolls (Yankees, Red Sox, etc.) than themselves. Since then, using sabermetrics has become a pillar of valuing players in Major League Baseball.

Beane and DePondesta effectively changed the landscape of evaluating professional baseball players. A very similiar change is occuring in the National Hockey League, albeit late to the table in comparision to the other major sports, it has gained a lot of traction in the last few years. Especially during the 2014-15 season, when the NHL partnered with SAP to create an enhanced statistical package that matched up with launch of a new website featuring advanced analytics. This also coincided with several prominent NHL franchises adding data analytics positions to their front office including Kyle Dubas (Toronto Maple Leafs), Tyler Dellow (Edmonton Oilers), and Sunny Mehta (New Jersey Devils). Since then, it has really taken off.

NHL Advanced Stats

Parity is at all time high in the NHL. When the trade deadline rolls around in late February, many teams still fancy themselves in the hunt for the Stanley Cup. To add to that effect, ever since the LA Kings won the Cup in 2012 as an 8 seed (and went 16-4 no less), many teams believe the whole mantra of “you just have to make it to the playoffs, and then anything can happen.”

This inevitably leads to a glut of ‘buyers’ (teams looking to pickup players to increase their chances of winning) at the trade deadline, but not nearly as many ‘sellers’ (teams that want to get of rid of players for future draft picks or young prospects). This, in conjunction with the salary cap that I’ll discuss later, means finding undervalued players is a quintessential way to try to gain an edge come playoff time.

My hope is that I am able to find the best statistics to understand player value vs how much that player makes.

Which leads me to my data science question: Can you build a model to predict NHL player’s salaries? What are the best predictors of how much a player will make?

If I can find undervalued players, that would ideally help a general manager make decisions on who to try to acquire.

For closing thoughts / next steps: NHL deputy commissioner Bill Daly confirmed to theScore that a full rollout of player and puck tracking is penciled in to debut during the 2019-20 season.

Imports

# Run all imports - some used, some not.
import pandas as pd
import numpy as np
import requests
import time
import regex as re
import matplotlib.pyplot as plt
import xgboost as xgb
import seaborn as sns
from scipy.stats import stats, iqr
from bs4 import BeautifulSoup
from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV, GridSearchCV, KFold
from sklearn.linear_model import LinearRegression, Lasso, Ridge, LassoCV, RidgeCV, ElasticNetCV
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error, accuracy_score

%matplotlib inline

Data Collection

NHL Salary Cap

Introduced after the full-season lockout of 04-05, the NHL currently has a salary cap in place. The main purposes of this salary cap is to curtail player salary growth to a reasonably manageable level, but also allow smaller-market teams to compete with larger-market teams. This cap is referred to as a ‘hard’ salary cap, meaning that each team can only spend up to that cap amount on a team of (at least) 24 players, up to a maximum of 50 players.

As one of my metrics for understanding player value will be value added vs cap hit, I’ll be using the NHL salary cap to gauge how much each player is making as a percentage of their teams’ total cap.

As I was only able to find player salary information going back to the 2011-2012 season, and the cap changes a variable amount every year, I’ll be manually entering the salary cap for each year. And in cases where I might need it in varying formats, I’ll be entering it once as a full year-to-year label (2011-2012), and once as a single year with the starting year representing the whole season (so, 2011-2012 would equal just 2011). There’s probably a better way to do it than this, but it’s moot as writing these both out doesn’t take much time at all.

salary_cap_y2y = {"2011-2012":64300000,
                  "2012-2013":70200000,
                  "2013-2014":64300000,
                  "2014-2015":69000000,
                  "2015-2016":71400000,
                  "2016-2017":73000000,
                  "2017-2018":75000000}

salary_cap_year = {"2011":64300000,
                  "2012":70200000,
                  "2013":64300000,
                  "2014":69000000,
                  "2015":71400000,
                  "2016":73000000,
                  "2017":75000000}

Scraper Code - Part I - Player Performance Data

Note: After further research, I ended up finding a better, more detailed site that included extra data points. This site (http://www.corsica.hockey/) allows its user to extract the information into .csv, rendering this loop, and the subsquent data pulled, redundant. However, since I put a great deal of effort into getting this loop to run properly, I’m going to leave it here to show my work. The code was original run, and it worked, but leaving it off of subsquent runs for timeliness.

I have to pull player salary from a different source than player performance data, so there will be two seperate data pulls and therefore loops.

These next dozen cells or so are going to be aimed at testing out functionality before creating a loop.

Relevant URLs that I’ll be pulling from.

First page:

https://www.hockey-reference.com/play-index/ppbp_finder.cgi?c2stat=&c4stat=&c2comp=&order_by_asc=&game_location=&c1comp=&year_min=2008&request=1&franch_id=&birth_country=&match=single&year_max=2018&c3comp=&report=ppbp&season_end=-1&c3stat=&order_by=player&season_start=1&c1val=&c3val=&c2val=&handed=&rookie=N&pos=S&describe_only=&c1stat=&situation_id=ev&c4val=&age_min=0&age_max=99&c4comp=&offset=0

Second page:

Notes:

There’s 100 players on each page, and the URL actually iterates by 100.
I ran through all possible pages to figure out where the iteration would have to end. Looks like that’s 9684 rows, so I’ll stop at 9600 for the URL, as it should include all players up until 9700.

# So first off, I'm going to write code that pulls just one page, and make sure that works.
# Once that's done, I'm going to put in the pull for the second page, to make sure I understand how to combine the two
# Then I'll write a loop based off of those two pulls to get the remaining data
# First page URL from above:
url = 'https://www.hockey-reference.com/play-index/ppbp_finder.cgi?c2stat=&c4stat=&c2comp=&order_by_asc=&game_location=&c1comp=&year_min=2008&request=1&franch_id=&birth_country=&match=single&year_max=2018&c3comp=&report=ppbp&season_end=-1&c3stat=&order_by=player&season_start=1&c1val=&c3val=&c2val=&handed=&rookie=N&pos=S&describe_only=&c1stat=&situation_id=ev&c4val=&age_min=0&age_max=99&c4comp=&offset=0'

# Second page:
url2 = 'https://www.hockey-reference.com/play-index/ppbp_finder.cgi?c2stat=&c4stat=&c2comp=&order_by_asc=&game_location=&c1comp=&year_min=2008&request=1&franch_id=&birth_country=&match=single&year_max=2018&c3comp=&report=ppbp&season_end=-1&c3stat=&order_by=player&season_start=1&c1val=&c3val=&c2val=&handed=&rookie=N&pos=S&describe_only=&c1stat=&situation_id=ev&c4val=&age_min=0&age_max=99&c4comp=&offset=100'

Pull request, status code check, and transform into BeautifulSoup

# Get request
res = requests.get(url)
# res2 = requests.get(url2)

# Confirm we got a successful response code
res.status_code

# Setup my soup object to parse out the data
soup = BeautifulSoup(res.content, 'lxml')
# soup2 = BeautifulSoup(res2.content, 'lxml')

# Take a brief look to make sure it pulled correctly
soup.text

# This find_all is exploratory, to understand how it's pulling, and how I can break it down further
soup.find_all('td', {'class':'left'})

# These are the individual scrapes, which I'll aggregate to loop and pull
# Creating them and printing them to make sure they work
player_name = soup.find_all('td', {'class':'left', 'data-stat':'player'})
pos = soup.find_all('td', {'class':'center', 'data-stat':'pos'})
team_id = soup.find_all('td', {'class':'left', 'data-stat':'team_id'})
season = soup.find_all('td', {'class':'left', 'data-stat':'season'})
games_played = soup.find_all('td', {'class':'right', 'data-stat':'games_played'})
goals = soup.find_all('td', {'class':'right', 'data-stat':'goals'})
assists = soup.find_all('td', {'class':'right', 'data-stat':'assists'})
points = soup.find_all('td', {'class':'right', 'data-stat':'points'})
corsi_for = soup.find_all('td', {'class':'right', 'data-stat':'corsi_for'})
corsi_against = soup.find_all('td', {'class':'right', 'data-stat':'corsi_against'})
corsi_pct = soup.find_all('td', {'class':'right', 'data-stat':'corsi_pct'})
corsi_rel_pct = soup.find_all('td', {'class':'right', 'data-stat':'corsi_rel_pct'})
corsi_per_60 = soup.find_all('td', {'class':'right', 'data-stat':'corsi_per_60'})
corsi_rel_per_60 = soup.find_all('td', {'class':'right', 'data-stat':'corsi_rel_per_60'})
fenwick_for = soup.find_all('td', {'class':'right', 'data-stat':'fenwick_for'})
fenwick_against = soup.find_all('td', {'class':'right', 'data-stat':'fenwick_against'})
fenwick_pct = soup.find_all('td', {'class':'right', 'data-stat':'fenwick_pct'})
fenwick_rel_pct = soup.find_all('td', {'class':'right', 'data-stat':'fenwick_rel_pct'})
on_ice_shot_pct = soup.find_all('td', {'class':'right', 'data-stat':'on_ice_shot_pct'})
on_ice_sv_pct = soup.find_all('td', {'class':'right', 'data-stat':'on_ice_sv_pct'})
pdo = soup.find_all('td', {'class':'right', 'data-stat':'pdo'})
zs_offense_pct = soup.find_all('td', {'class':'right', 'data-stat':'zs_offense_pct'})
zs_defense_pct = soup.find_all('td', {'class':'right', 'data-stat':'zs_defense_pct'})
toi_pbp_avg = soup.find_all('td', {'class':'right', 'data-stat':'toi_pbp_avg'})
faceoff_wins = soup.find_all('td', {'class':'right', 'data-stat':'faceoff_wins'})
faceoff_losses = soup.find_all('td', {'class':'right', 'data-stat':'faceoff_losses'})
faceoff_percentage = soup.find_all('td', {'class':'center', 'data-stat':'faceoff_percentage'})
hits = soup.find_all('td', {'class':'right', 'data-stat':'hits'})
blocks = soup.find_all('td', {'class':'right', 'data-stat':'blocks'})
takeaways = soup.find_all('td', {'class':'right', 'data-stat':'takeaways'})
giveaways = soup.find_all('td', {'class':'right', 'data-stat':'giveaways'})

# This cell to make sure each of my variables got pulled in correctly
# And that I can pull the data out as expected
# Print player 1's stats essentially
print(f'''
{player_name[0].text}
{pos[0].text}
{team_id[0].text}
{season[0].text}
{games_played[0].text}
{goals[0].text}
{assists[0].text}
{points[0].text}
{corsi_for[0].text}
{corsi_against[0].text}
{corsi_pct[0].text}
{corsi_rel_pct[0].text}
{corsi_per_60[0].text}
{corsi_rel_per_60[0].text}
{fenwick_for[0].text}
{fenwick_against[0].text}
{fenwick_pct[0].text}
{fenwick_rel_pct[0].text}
{on_ice_shot_pct[0].text}
{on_ice_sv_pct[0].text}
{pdo[0].text}
{zs_offense_pct[0].text}
{zs_defense_pct[0].text}
{toi_pbp_avg[0].text}
{faceoff_wins[0].text}
{faceoff_losses[0].text}
{faceoff_percentage[0].text}
{hits[0].text}
{blocks[0].text}
{takeaways[0].text}
{giveaways[0].text}
''')

# This cell is to test putting together a dataframe from many lists
df_test_player1 = pd.DataFrame(
    {'player_name': player_name[0].text,
    'pos': pos[0].text,
    'team_id': team_id[0].text,
    'season': season[0].text,
    'games_played': games_played[0].text,
    'goals': goals[0].text,
    'assists': assists[0].text,
    'points': points[0].text,
    'corsi_for': corsi_for[0].text,
    'corsi_against': corsi_against[0].text,
    'corsi_pct': corsi_pct[0].text,
    'corsi_rel_pct': corsi_rel_pct[0].text,
    'corsi_per_60': corsi_per_60[0].text,
    'corsi_rel_per_60': corsi_rel_per_60[0].text,
    'fenwick_for': fenwick_for[0].text,
    'fenwick_against': fenwick_against[0].text,
    'fenwick_pct': fenwick_pct[0].text,
    'fenwick_rel_pct': fenwick_rel_pct[0].text,
    'on_ice_shot_pct': on_ice_shot_pct[0].text,
    'on_ice_sv_pct': on_ice_sv_pct[0].text,
    'pdo': pdo[0].text,
    'zs_offense_pct': zs_offense_pct[0].text,
    'zs_defense_pct': zs_defense_pct[0].text,
    'toi_pbp_avg': toi_pbp_avg[0].text,
    'faceoff_wins': faceoff_wins[0].text,
    'faceoff_losses': faceoff_losses[0].text,
    'faceoff_percentage': faceoff_percentage[0].text,
    'hits': hits[0].text,
    'blocks': blocks[0].text,
    'takeaways': takeaways[0].text,
    'giveaways': giveaways[0].text}, index=[0])

# Take a look at the df, compare it to the website to make sure everything lines up correctly
df_test_player1.T

# Keeping this here for a visualization of how zip works,
# and how I might look through to aggregate my future lists together in a dataframe
for i, j in zip(player_name, season):
    print(i.text, j.text)

goals = soup.find_all('td', {'class':'right', 'data-stat':'goals'})

# Testing the url + next_get portion of the request pull
next_get = str(100)
url = 'https://www.hockey-reference.com/play-index/ppbp_finder.cgi?c2stat=&c4stat=&c2comp=&order_by_asc=&game_location=&c1comp=&year_min=2008&request=1&franch_id=&birth_country=&match=single&year_max=2018&c3comp=&report=ppbp&season_end=-1&c3stat=&order_by=player&season_start=1&c1val=&c3val=&c2val=&handed=&rookie=N&pos=S&describe_only=&c1stat=&situation_id=ev&c4val=&age_min=0&age_max=99&c4comp=&offset='

res = requests.get(url+next_get)

# Testing creation of DF
df_puck = pd.DataFrame([], columns=['player_name', 'pos', 'team_id', 'season', 'games_played', 'goals', 'assists', 'points', 'corsi_for', 'corsi_against', 'corsi_pct', 'corsi_rel_pct', 'corsi_per_60', 'corsi_rel_per_60', 'fenwick_for', 'fenwick_against', 'fenwick_pct', 'fenwick_rel_pct', 'on_ice_shot_pct', 'on_ice_sv_pct', 'pdo', 'zs_offense_pct', 'zs_defense_pct', 'toi_pbp_avg', 'faceoff_wins', 'faceoff_losses', 'faceoff_percentage', 'hits', 'blocks', 'takeaways', 'giveaways'])

# Testing appending
df_puck = df_puck.append(df_test_player1, )

df_puck

# Scraper loop
# Original URL
url = 'https://www.hockey-reference.com/play-index/ppbp_finder.cgi?c2stat=&c4stat=&c2comp=&order_by_asc=&game_location=&c1comp=&year_min=2008&request=1&franch_id=&birth_country=&match=single&year_max=2018&c3comp=&report=ppbp&season_end=-1&c3stat=&order_by=player&season_start=1&c1val=&c3val=&c2val=&handed=&rookie=N&pos=S&describe_only=&c1stat=&situation_id=ev&c4val=&age_min=0&age_max=99&c4comp=&offset='
df_puck = pd.DataFrame([], columns=['player_name', 'pos', 'team_id', 'season', 'games_played', 'goals', 'assists', 'points', 'corsi_for', 'corsi_against', 'corsi_pct', 'corsi_rel_pct', 'corsi_per_60', 'corsi_rel_per_60', 'fenwick_for', 'fenwick_against', 'fenwick_pct', 'fenwick_rel_pct', 'on_ice_shot_pct', 'on_ice_sv_pct', 'pdo', 'zs_offense_pct', 'zs_defense_pct', 'toi_pbp_avg', 'faceoff_wins', 'faceoff_losses', 'faceoff_percentage', 'hits', 'blocks', 'takeaways', 'giveaways'])

# See logic above for why I chose these numbers
for i in range(0, 9700, 100):

    # Create lists fresh on each loop
    player_name_list = []
    pos_list = []
    team_id_list = []
    season_list = []
    games_played_list = []
    goals_list = []
    assists_list = []
    points_list = []
    corsi_for_list = []
    corsi_against_list = []
    corsi_pct_list = []
    corsi_rel_pct_list = []
    corsi_per_60_list = []
    corsi_rel_per_60_list = []
    fenwick_for_list = []
    fenwick_against_list = []
    fenwick_pct_list = []
    fenwick_rel_pct_list = []
    on_ice_shot_pct_list = []
    on_ice_sv_pct_list = []
    pdo_list = []
    zs_offense_pct_list = []
    zs_defense_pct_list = []
    toi_pbp_avg_list = []
    faceoff_wins_list = []
    faceoff_losses_list = []
    faceoff_percentage_list = []
    hits_list = []
    blocks_list = []
    takeaways_list = []
    giveaways_list = []

    # Iteration to create end of URL
    next_get = str(i)

    # Request get
    res = requests.get(url+next_get)

    # Create into bs4 object
    soup = BeautifulSoup(res.content, 'lxml')

    # Breakdown soup via find_all into its various pieces
    player_name = soup.find_all('td', {'class':'left', 'data-stat':'player'})
    pos = soup.find_all('td', {'class':'center', 'data-stat':'pos'})
    team_id = soup.find_all('td', {'class':'left', 'data-stat':'team_id'})
    season = soup.find_all('td', {'class':'left', 'data-stat':'season'})
    games_played = soup.find_all('td', {'class':'right', 'data-stat':'games_played'})
    goals = soup.find_all('td', {'class':'right', 'data-stat':'goals'})
    assists = soup.find_all('td', {'class':'right', 'data-stat':'assists'})
    points = soup.find_all('td', {'class':'right', 'data-stat':'points'})
    corsi_for = soup.find_all('td', {'class':'right', 'data-stat':'corsi_for'})
    corsi_against = soup.find_all('td', {'class':'right', 'data-stat':'corsi_against'})
    corsi_pct = soup.find_all('td', {'class':'right', 'data-stat':'corsi_pct'})
    corsi_rel_pct = soup.find_all('td', {'class':'right', 'data-stat':'corsi_rel_pct'})
    corsi_per_60 = soup.find_all('td', {'class':'right', 'data-stat':'corsi_per_60'})
    corsi_rel_per_60 = soup.find_all('td', {'class':'right', 'data-stat':'corsi_rel_per_60'})
    fenwick_for = soup.find_all('td', {'class':'right', 'data-stat':'fenwick_for'})
    fenwick_against = soup.find_all('td', {'class':'right', 'data-stat':'fenwick_against'})
    fenwick_pct = soup.find_all('td', {'class':'right', 'data-stat':'fenwick_pct'})
    fenwick_rel_pct = soup.find_all('td', {'class':'right', 'data-stat':'fenwick_rel_pct'})
    on_ice_shot_pct = soup.find_all('td', {'class':'right', 'data-stat':'on_ice_shot_pct'})
    on_ice_sv_pct = soup.find_all('td', {'class':'right', 'data-stat':'on_ice_sv_pct'})
    pdo = soup.find_all('td', {'class':'right', 'data-stat':'pdo'})
    zs_offense_pct = soup.find_all('td', {'class':'right', 'data-stat':'zs_offense_pct'})
    zs_defense_pct = soup.find_all('td', {'class':'right', 'data-stat':'zs_defense_pct'})
    toi_pbp_avg = soup.find_all('td', {'class':'right', 'data-stat':'toi_pbp_avg'})
    faceoff_wins = soup.find_all('td', {'class':'right', 'data-stat':'faceoff_wins'})
    faceoff_losses = soup.find_all('td', {'class':'right', 'data-stat':'faceoff_losses'})
    faceoff_percentage = soup.find_all('td', {'class':'center', 'data-stat':'faceoff_percentage'})
    hits = soup.find_all('td', {'class':'right', 'data-stat':'hits'})
    blocks = soup.find_all('td', {'class':'right', 'data-stat':'blocks'})
    takeaways = soup.find_all('td', {'class':'right', 'data-stat':'takeaways'})
    giveaways = soup.find_all('td', {'class':'right', 'data-stat':'giveaways'})

    # Add the various soup objects into a new dataframe
    for a in range(0, len(player_name), 1):
        if a == 0:
            df_append = pd.DataFrame(
            {'player_name': player_name[a].text,
            'pos': pos[a].text,
            'team_id': team_id[a].text,
            'season': season[a].text,
            'games_played': games_played[a].text,
            'goals': goals[a].text,
            'assists': assists[a].text,
            'points': points[a].text,
            'corsi_for': corsi_for[a].text,
            'corsi_against': corsi_against[a].text,
            'corsi_pct': corsi_pct[a].text,
            'corsi_rel_pct': corsi_rel_pct[a].text,
            'corsi_per_60': corsi_per_60[a].text,
            'corsi_rel_per_60': corsi_rel_per_60[a].text,
            'fenwick_for': fenwick_for[a].text,
            'fenwick_against': fenwick_against[a].text,
            'fenwick_pct': fenwick_pct[a].text,
            'fenwick_rel_pct': fenwick_rel_pct[a].text,
            'on_ice_shot_pct': on_ice_shot_pct[a].text,
            'on_ice_sv_pct': on_ice_sv_pct[a].text,
            'pdo': pdo[a].text,
            'zs_offense_pct': zs_offense_pct[a].text,
            'zs_defense_pct': zs_defense_pct[a].text,
            'toi_pbp_avg': toi_pbp_avg[a].text,
            'faceoff_wins': faceoff_wins[a].text,
            'faceoff_losses': faceoff_losses[a].text,
            'faceoff_percentage': faceoff_percentage[a].text,
            'hits': hits[a].text,
            'blocks': blocks[a].text,
            'takeaways': takeaways[a].text,
            'giveaways': giveaways[a].text}, index=[i])
        else:
            df_append = df_append.append(
            {'player_name': player_name[a].text,
            'pos': pos[a].text,
            'team_id': team_id[a].text,
            'season': season[a].text,
            'games_played': games_played[a].text,
            'goals': goals[a].text,
            'assists': assists[a].text,
            'points': points[a].text,
            'corsi_for': corsi_for[a].text,
            'corsi_against': corsi_against[a].text,
            'corsi_pct': corsi_pct[a].text,
            'corsi_rel_pct': corsi_rel_pct[a].text,
            'corsi_per_60': corsi_per_60[a].text,
            'corsi_rel_per_60': corsi_rel_per_60[a].text,
            'fenwick_for': fenwick_for[a].text,
            'fenwick_against': fenwick_against[a].text,
            'fenwick_pct': fenwick_pct[a].text,
            'fenwick_rel_pct': fenwick_rel_pct[a].text,
            'on_ice_shot_pct': on_ice_shot_pct[a].text,
            'on_ice_sv_pct': on_ice_sv_pct[a].text,
            'pdo': pdo[a].text,
            'zs_offense_pct': zs_offense_pct[a].text,
            'zs_defense_pct': zs_defense_pct[a].text,
            'toi_pbp_avg': toi_pbp_avg[a].text,
            'faceoff_wins': faceoff_wins[a].text,
            'faceoff_losses': faceoff_losses[a].text,
            'faceoff_percentage': faceoff_percentage[a].text,
            'hits': hits[a].text,
            'blocks': blocks[a].text,
            'takeaways': takeaways[a].text,
            'giveaways': giveaways[a].text}, ignore_index = True)

    # Kept getting timeout errors, so added a sleep to offset
    time.sleep(3)

    df_puck = df_puck.append(df_append, ignore_index = True)
    df_puck.to_csv('hockey_data.csv', index = True)

# This pulls out the first year (first_year-second_year) to simplify that data point
df_puck['year'] = df_puck['season'].apply(lambda x: int(x[:4]))

df_puck.head()

Scraper Code - Part II - Player Cap Hits

Note: As the dollar sign is used in latex/markdown cells for creating formulas, I can’t write it here, so all stated monetary values are in USD.

An important distinction to make before diving in here: a player’s salary can, and usually is, different then their actual cap hit. A player’s cap hit is the average annual value over the entire length of their contract.

So for example, in 2007, Pittsburgh Penguins’ captain Sidney Crosby signed a 12-year, 104.4 million dollar contract. The average annual value comes out to 8.7 million/year, which is how much his salary counts against the cap. However, the deal is not evenly structured throughout the contract to pay Sid the Kid 8.7 mil/yr. He was paid 12 mil/yr the first 3 years of the contract, but will only be paid 3 mil/yr the last 3 years of the contract. The years in between do not vary as much as either tail of the contract, but the point is that player yearly salary =/= their salary cap hit.

As the cap hit is truly what matters for building NHL teams, and it actually helps ‘normalize’ player salaries across the board, that’s the more important measurement I’ll be using here.

Base URL pulling from: https://www.spotrac.com/nhl/rankings/cap-hit/
Subsquent URLs look like this: https://www.spotrac.com/nhl/rankings/YEAR/cap-hit/
Where YEAR = the year the season opened in.

These next dozen cells or so are going to be aimed at testing out functionality before creating a loop.

# Base URL
url_base_cap_hit = 'https://www.spotrac.com/nhl/rankings/cap-hit/'

# Generate request and BeautifulSoup object off of that
res = requests.get(url_base_cap_hit)
soup = BeautifulSoup(res.content, 'lxml')

# Take a look at this nastiness just to make sure it ran properly
soup.text

' \n\n\n\n\n\nNHL Salary Rankings | Spotrac\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    window._mNHandle = window._mNHandle || {};\n    window._mNHandle.queue = window._mNHandle.queue || [];\n    medianet_versionId = "3121199"; \n\n\n\n\n\n\n\n\n\n\nCookie Settings\n\n\n\nAccept Cookies\n\n\n\nClose\n\n\n\n \nWe use cookies to offer you a better browsing experience, analyse site traffic, and serve targeted ads. Read how we use cookies and how you can control them in our “Cookie Settings”. By using our site, you consent to our use of cookies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nspotrac\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNFL\n\nTeam Salary Caps\nPositional Spending\nContracts\nSalary Rankings\nFree Agents\n\n\nTrackers & Tools\n\n» Market Values\n» Fines/Suspensions\n» IR Tracker\n» Depth Charts Tracker\n» Draft Tracker\n» Options\n» College Tracker\n\n\nBest Values\nTransactions\n\n\n\n\nNBA\n\nTeam Cap Tracker\nPositional Payrolls\nContracts\nSalary Rankings\nFree Agents\n\n\nTrackers & Tools\n\n» Depth Charts\n» Options\n» Fines/Suspensions\n» Draft Tracker\n» College Tracker\n» Trade Tracker\n» Country Tracker\n\n\nBest Values\nTransactions\n\n\n\n\nMLB\n\nTeam Payrolls\nPositional Payrolls\nContracts\nSalary Rankings\nFree Agents\n\n\nTrackers & Tools\n\n» Fines/Suspensions\n» Disabled List\n» Options Tracker\n» Trade Tracker\n» Arbitration Tracker\n\n\nBest Values\nTransactions\n\n\n\n\nNHL\n\nTeam Salary Caps\nPositional Payrolls\nContracts\nSalary Rankings\nFree Agents\n\n\nTrackers & Tools\n\n» IR Tracker\n» Options\n» Fines/Suspensions\nDraft Tracker\n» Trade Tracker\n» College/Junior Team Tracker\n» Country Tracker\n\n\nBest Values\nTransactions\n\n\n\n\nEPL\n\nTeam Payrolls\nPositional Payrolls\n\n\nSalary Rankings\n\n» Average Salary\n» Weekly Wage\n» Contract Value\n» Transfer Feer\n\n\nTransfers\nFree Agents\nTransactions\n\n\n\n\nMLS\n\nTeam Payrolls\nPositional Payrolls\nSalary Rankings\n\n\nTrackers & Tools\n\n» Team Caps\n» Draft Tracker\n» College Tracker\n» Trade Tracker\n\n\nTransactions\n\n\n\n\nMore\n\nRadio Podcast\nFantasy Contests\nResearch & News\n\n\n\n\nPREMIUM\n\n Sign In\n Register\n\n\n\n Contact Us\n\n\n Twitter\n\n\n Facebook\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFollow Spotrac\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNFL \n\n\n\n\nTeam Salary Caps\n\n\n\nArizona Cardinals\nAtlanta Falcons\nBaltimore Ravens\nBuffalo Bills\nCarolina Panthers\nChicago Bears\nCincinnati Bengals\nCleveland Browns\n\n\nDallas Cowboys\nDenver Broncos\nDetroit Lions\nGreen Bay Packers\nHouston Texans\nIndianapolis Colts\nJacksonville Jaguars\nKansas City Chiefs\n\n\nLos Angeles Chargers\nLos Angeles Rams\nMiami Dolphins\nMinnesota Vikings\nNew England Patriots\nNew Orleans Saints\nNew York Giants\nNew York Jets\n\n\nOakland Raiders\nPhiladelphia Eagles\nPittsburgh Steelers\nSan Francisco 49ers\nSeattle Seahawks\nTampa Bay Buccaneers\nTennessee Titans\nWashington Redskins\n\n\n\n\n\n\nPositional Payrolls\n\n\n\nOffense\nQuarterback\nRunning Back\nFullback\nWide Receiver\nTight End\nOffensive Line\nTackle\nLeft Tackle\nRight Tackle\n\n\nGuard\nCenter\nDefense\nDefensive Line\nDefensive End\nDefensive Tackle\nLinebacker\nOutside Linebacker\nInside Linebacker\nEdge\n\n\nSecondary\nCornerback\nSafety\nFree Safety\nStrong Safety\nSpecial Teams\nKicker\nPunter\nLong Snapper\n\n\n\n\n\nContracts\n\n\n\nContracts By Team\nContracts By Position\nLargest Contracts\nLongest Contracts\nContracts on IR\nExpiring Contracts\n\n\nCumulative Cash per Contract\nContract Breakdowns by Years\nRookie Contracts\nVeteran Contracts\nContracts by College\nContracts by Age\n\n\n\n\n\nSalary Rankings\n\n\n\nTop Cap Hits\nTop Base Salaries\nAverage Salaries\nTop Annual Cash\nTop Guaranteed Money\nTop Signing Bonuses\nTop Dead Cap\nCareer Earnings\n\n\nSalaries by Team\nSalaries by Position\nCap Hits by Team\nCap Hits by Position\nFranchise Tags\n\n\n\n\n\nTrackers & Tools\n\n\nTeam Salary Cap Tracker\nFree Agent Tracker\nFines & Suspensions\nInjured Reserve\nTrade Tracker\nDraft Tracker\nOption Tracker\nCollege Tracker\nAgents Tracker\n\n\nCurrent Market Values\nDepth Charts Tracker\nVisual Roster Spending\nOffseason Spending\nContract Builder\nRoster/Salary Cap Manager\n\n\n\nBest Values\n\n\nBest Value Players\nBest Value Offensive Players\nBest Value Defensive Players\n\xa0\nBest Value Teams\n\n\n\n\nStatistics\n\n\nStatistics Leaders\nPlayer Statistics\nTeam Statistics\n\n\n\n\nTransactions\n\n\nNFL Transactions\nTrade Tracker\nFree Agent Signings\nFuture Bonuses & Guarantees\n\n\n\n\nPremium Tools\n\n\nContract Breakdowns by Year\nMulti-Year Salary Tool\nFinancial Team Comparison\nFinancial Player Comparison\nYearly % Change\n\n\n\n\n\n\n\n\n\nNBA \n\n\n\n\nTeam Salary Caps\n\n\n\nAtlanta Hawks\nBoston Celtics\nBrooklyn Nets\nCharlotte Hornets\nChicago Bulls\nCleveland Cavaliers\nDallas Mavericks\nDenver Nuggets\nDetroit Pistons\n\n\nGolden State Warriors\nHouston Rockets\nIndiana Pacers\nLos Angeles Clippers\nLos Angeles Lakers\nMemphis Grizzlies\nMiami Heat\nMilwaukee Bucks\nMinnesota Timberwolves\n\n\nNew Orleans Pelicans\nNew York Knicks\nOklahoma City Thunder\nOrlando Magic\nPhiladelphia 76ers\nPhoenix Suns\nPortland Trail Blazers\nSacramento Kings\nSan Antonio Spurs\n\n\nToronto Raptors\nUtah Jazz\nWashington Wizards\n\n\n\n\nPositional Payrolls\n\n\n\nForward\nPoint Guard\nShooting Guard\nGuard\nSmall Forward\n\n\nPower Forward\nCenter\n\n\n\n\n\nContracts\n\n\n\nContracts By Team\nContracts By Position\nContracts by Draft\nContracts by Age\nExpiring Contracts\n\n\nLargest Active\nLongest Active\nRookie Contracts\nVeteran Contracts\n\n\n\n\n\nSalary Rankings\n\n\n\nTop Cap Hits\nTop Base Salaries\nAverage Salaries\nDead Cap\nCareer Earnings\n\n\nSalaries by Team\nSalaries by Position\nCap Hits by Team\nCap Hits by Position\n\n\n\n\n\nTrackers & Tools\n\n\nTeam Salary Cap Tracker\nFree Agent Tracker\nFines & Suspensions\nTeam Depth Charts\nTrade Tracker\n\n\nRookie Scale\nDraft Tracker\nOption Tracker\nCollege Tracker\nCountry Tracker\n\n\n\n\nBest Values\n\n\nBest Value Players\nBest Value Teams\n\n\n\n\nStatistics\n\n\nStatistics Leaders\nPlayer Statistics\nTeam Statistics\n\n\n\n\nTransactions\n\n\nNBA Transactions\nTrade Tracker\nFree Agent Signings\n\n\n\n\nPremium Tools\n\n\nContract Breakdowns by Year\nMulti-Year Salary Tool\nFinancial Team Comparison\nFinancial Player Comparison\n\n\n\n\n\n\n\n\n\nMLB \n\n\n\n\nTeam Payrolls\n\n\n\nArizona Diamondbacks\nAtlanta Braves\nBaltimore Orioles\nBoston Red Sox\nChicago Cubs\nChicago White Sox\nCincinnati Reds\nCleveland Indians\nColorado Rockies\nDetroit Tigers\n\n\nHouston Astros\nKansas City Royals\nLos Angeles Angels of Anaheim\nLos Angeles Dodgers\nMiami Marlins\nMilwaukee Brewers\nMinnesota Twins\nNew York Mets\nNew York Yankees\nOakland Athletics\n\n\nPhiladelphia Phillies\nPittsburgh Pirates\nSan Diego Padres\nSan Francisco Giants\nSeattle Mariners\nSt. Louis Cardinals\nTampa Bay Rays\nTexas Rangers\nToronto Blue Jays\nWashington Nationals\n\n\n\n\n\n\n\nPositional Payrolls\n\n\n\nCatcher\nInfielders\n1st Base\n2nd Base\n3rd Base\nShortstop\nOutfielders\nRight Field\nCenter Field\nLeft Field\n\n\nDesignated Hitter\nPitchers\nStarting Pitcher\nRelief Pitcher\nCloser\n\n\n\n\n\nContracts\n\n\nContracts By Team\nContracts By Position\nContracts by College\nContracts by Age\nExpiring Contracts\n\n\nLargest Active\nLongest Active\n\n\n\n\nSalary Rankings\n\n\n\nTop Payroll Salaries\nTop Base Salaries\nAverage Salaries\nAnnual Cash Earnings\nTop Signing Bonuses\nCareer Earnings\n\n\nSalaries by Team\nSalaries by Position\nSalaries by Season\n\n\n\n\n\nTrackers & Tools\n\n\nTeam Payroll Tracker\nFree Agent Tracker\nFines & Suspensions\nDisabled List\nTrade Tracker\nArbitration Tracker\nOption Tracker\nDraft Tracker\nSchedule Tracker\n\n\nInternational Signings\nRoster/Payroll Manager\nOffseason Spending\nCollege Tracker\nCountry Tracker\nDepth Charts Tool\n\n\n\nValue Rankings\n\n\nBest Value Batters\nBest Value Starting Pitchers\nBest Value Relief Pitchers\n\xa0\nBest Value Team\n\n\n\n\nStatistics\n\n\nStatistics Leaders\nPlayer Statistics\nTeam Statistics\n\nSingle Season Record Comparison\nRun/Salary Differential\n\n\n\n\nTransactions\n\n\nMLB Transactions\nTrade Tracker\nFree Agent Signings\n\n\n\n\nPremium Tools\n\n\nContract Breakdowns by Year\nMulti-Year Salary Tool\nFinancial Team Comparison\nFinancial Player Comparison\nPlayer Valuation\n\n\n\n\n\n\n\n\n\nNHL \n\n\n\n\nTeam Salary Caps\n\n\n\nAnaheim Ducks\nArizona Coyotes\nBoston Bruins\nBuffalo Sabres\nCalgary Flames\nCarolina Hurricanes\nChicago Blackhawks\nColorado Avalanche\nColumbus Blue Jackets\nDallas Stars\n\n\nDetroit Red Wings\nEdmonton Oilers\nFlorida Panthers\nLos Angeles Kings\nMinnesota Wild\nMontreal Canadiens\nNashville Predators\nNew Jersey Devils\nNew York Islanders\nNew York Rangers\n\n\nOttawa Senators\nPhiladelphia Flyers\nPittsburgh Penguins\nSan Jose Sharks\nSt Louis Blues\nTampa Bay Lightning\nToronto Maple Leafs\nVancouver Canucks\nVegas Golden Knights\nWashington Capitals\n\n\nWinnipeg Jets\n\n\n\n\nPositional Payrolls\n\n\n\nForward\nCenter\nLeft Wing\nRight Wing\nDefenseman\nGoaltender\n\n\n\n\nContracts\n\n\n\nContracts By Team\nContracts By Position\nContracts by Country\nFree Agent Contracts\nExpiring Contracts\n\n\nLargest Active\nLongest Active\nRookie Contracts\nVeteran Contracts\n\n\n\n\n\nSalary Rankings\n\n\n\nTop Cap Hits\nTop Base Salaries\nTop Annual Cash\nSigning Bonuses\n\n\nSalaries by Team\nSalaries by Position\nCap Hits by Team\nCap Hits by Position\nEarnings by Country\n\n\n\n\n\nTrackers & Tools\n\n\nTeam Cap Tracker\nFree Agent Tracker\nFines & Suspensions\nInjured Reserve\nDraft Tracker\nTrade Tracker\nCollege/Junior Team Tracker\nCountry Tracker\nBuyout Calculator\n\n\n\n\n\nBest Values\n\n\nBest Value Players\n\xa0\nBest Value Teams\n\n\n\n\nStatistics\n\n\nStatistics Leaders\nPlayer Statistics\nTeam Statistics\n\n\n\n\nTransactions\n\n\nNHL Transactions\nTrade Tracker\nFree Agent Signings\n\n\n\n\nPremium Tools\n\n\nContract Breakdowns by Year\nMulti-Year Salary Tool\nFinancial Team Comparison\nFinancial Player Comparison\nBuyout Calculator\n\n\n\n\n\n\n\n\n\nEPL \n\n\n\n\nTeam Payrolls\n\n\n\nAFC Bournemouth\nArsenal F.C.\nBrighton & Hove Albion\nBurnley F.C.\nCardiff City F.C.\nChelsea F.C.\nCrystal Palace\nEverton F.C.\nFulham F.C.\nHuddersfield Town\n\n\nLeicester City\nLiverpool F.C.\nManchester City F.C.\nManchester United F.C.\nNewcastle United F.C.\nSouthampton F.C.\nTottenham Hotspur F.C.\nWatford\nWest Ham United F.C.\nWolverhampton Wanderers F.C.\n\n\n\n\n\nContracts\n\nPositional Payrolls\n\n\n\nForward\nMidfielder\nDefender\nGoalkeeper\n\n\n\n\n\nSalary Rankings\n\n\nAverage Salary\nContract Value\nWeekly Wage\nTransfer Fee\n\n\n\nFree Agents\nTransfers\nTransactions\n\n\n\n\n\n\nMLS \n\n\n\n\nTeam Payrolls\n\n\n\nAtlanta United FC\nChicago Fire\nColorado Rapids\nColumbus Crew\nD.C. United\nFC Dallas\nHouston Dynamo\nLos Angeles FC\nLos Angeles Galaxy\nMinnesota United FC\n\n\nMontreal Impact\nNew England Revolution\nNew York City FC\nNew York Red Bulls\nOrlando City\nPhiladelphia Union\nPortland Timbers\nReal Salt Lake\nSan Jose Earthquakes\nSeattle Sounders FC\n\n\nSporting Kansas City\nToronto FC\nVancouver Whitecaps FC\n\n\n\n\nPositional Payrolls\n\n\n\nForward\nMidfielder\nDefender\nGoalkeeper\n\n\n\n\n\nSalary Rankings\n\n\nTrackers\n\n\nTeam Salary Cap Tracker\nTrade Tracker\nDraft Tracker\nCollege Tracker\n\n\n\n\n\n\nTransactions\n\n\nMLS Transactions\nTrade Tracker\n\n\n\n\n\n\n\n\nMore \n\n\nSpotrac Radio Podcast\nFantasy Contests\nResearch & News\n\n\n\n\nPREMIUM \n\n\nSign In\nRegister\n\n\n\n\n\n\n Tweet\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHIDE X\n\n\nAd-FREE Experience\nHistorical Contracts\nThe Project Sum Podcast\n\n\nAdvanced Data Portal (Soon!)\nComplete League Breakdowns\nCustom Team/Player Comparisons\n\n\n\nSign Up \n\n\n\n\n\n\n\n\n\n\nClose\nCookie Settings\n\n\n\nYour Privacy »\nNecessary Cookies »\nTraffic & Performance »\nMarketing & Advertising »\nPrivacy Policy »\n\n\nYour Privacy\nWhen you visit any web site, it may store or retrieve information on your browser, mostly in the form of cookies. This information might be about you, your preferences or your device and is mostly used to make the site work as you expect it to. The information does not usually directly identify you, but it can give you a more personalised web experience. Because we respect your right to privacy, you can choose not to allow some types of cookies. Click on the different category headings to find out more and change our default settings. However, blocking some types of cookies may impact your experience of the site and the services we are able to offer.\n\n\nNecessary Cookies\nThese cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will not then work.\n\n\nTraffic & Performance\nThese cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance.\n\n\nMarketing & Advertising\nThese cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. They do not store directly personal information, but are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising. Also, by disabling these cookies you will also disable banner ads served by Google Adsense on this website..\n\n\nPrivacy Policy\nFor more information about these items, view our complete privacy policy.Read More\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTeams\nTeam Caps\nPositional Spending\nContracts\nRankings \n\nCap Hits\nAverage Salary\nCash Salaries\nContract Values\nContract Lengths\nSigning Bonus\n\n\nFree Agents\nTools/Trackers \n\nBuyout Calculator\nTrade Tracker\nDraft Tracker\nInjured Reserve\nFines/Suspensions\nPlayers by Country\n\n\nTransactions\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n NHL Salary Rankings \n\nListing the top salaries, cap hits, cash, earnings, contracts, and bonuses, for all active NHL players.\n\n\n\n (__scads = window.__scads || []).push({"z":3907,"targetId":"switch_placeholder_7053b41429e7678723e03f4affaf598b","domain":"delivery.switchadhub.com","width":"0","height":"0"}); \n\n\n\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\n\n\n2023\n2022\n2021\n2020\n2019\n2018\n2017\n2016\n2015\n2014\n2013\n2012\n2011\n\n\n\n\n\n\nCap Hits\nTotal Salary\n\nAverage\nContract Length\nContract Value\n\nCareer Earnings\n\n\n\n\n\n\n- Teams -\nANA\nAZ\nBOS\nBUF\nCGY\nCAR\nCHI\nCOL\nCBJ\nDAL\nDET\nEDM\nFLA\nLAK\nMIN\nMTL\nNSH\nNJD\nNYI\nNYR\nOTT\nPHI\nPIT\nSJS\nSTL\nTBL\nTOR\nVAN\nVGK\nWAS\nWPG\n\n\n\n\n\n\nAll Positions\n\xa0\xa0\xa0\xa0Forward\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Center\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Left Wing\n\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0Right Wing\n\xa0\xa0\xa0\xa0Defenseman\n\xa0\xa0\xa0\xa0Goaltender\n\n\n\n\n\n\nAll Types\nRookies\nVeterans\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n2018   Cap Hit Rankings\n\n\n\n\n\n\n\nPlayer\n cap hit\n\n\n\n\n1\n\n\nConnor McDavid\nCenter\n\n$12,500,000  \n\n\n2\n\n\nJohn Tavares\nCenter\n\n$11,000,000  \n\n\n3\n\n\nPatrick Kane\nRight Wing\n\n$10,500,000  \n\n\n\n\n\nJonathan Toews\nCenter\n\n$10,500,000  \n\n\n\n\n\nCarey Price\nGoaltender\n\n$10,500,000  \n\n\n6\n\n\nAnze Kopitar\nCenter\n\n$10,000,000  \n\n\n\n\n\nJack Eichel\nCenter\n\n$10,000,000  \n\n\n8\n\n\nAlex Ovechkin\nLeft Wing\n\n$9,538,462  \n\n\n9\n\n\nEvgeni Malkin\nCenter\n\n$9,500,000  \n\n\n\n\n\nJamie Benn\nLeft Wing\n\n$9,500,000  \n\n\n11\n\n\nP.K. Subban\nDefenseman\n\n$9,000,000  \n\n\n12\n\n\nSidney Crosby\nCenter\n\n$8,700,000  \n\n\n13\n\n\nCorey Perry\nRight Wing\n\n$8,625,000  \n\n\n14\n\n\nLeon Draisaitl\nCenter\n\n$8,500,000  \n\n\n\n\n\nHenrik Lundqvist\nGoaltender\n\n$8,500,000  \n\n\n\n\n\nSteven Stamkos\nCenter\n\n$8,500,000  \n\n\n17\n\n\nClaude Giroux\nCenter\n\n$8,275,000  \n\n\n18\n\n\nJakub Voracek\nRight Wing\n\n$8,250,063  \n\n\n19\n\n\nRyan Getzlaf\nCenter\n\n$8,250,000  \n\n\n20\n\n\nRyan Johansen\nCenter\n\n$8,000,000  \n\n\n\n\n\nBrent Burns\nDefenseman\n\n$8,000,000  \n\n\n\n\n\nJohn Carlson\nDefenseman\n\n$8,000,000  \n\n\n23\n\n\nVictor Hedman\nDefenseman\n\n$7,875,000  \n\n\n24\n\n\nShea Weber\nDefenseman\n\n$7,857,143  \n\n\n25\n\n\nEvgeny Kuznetsov\nCenter\n\n$7,800,000  \n\n\n26\n\n\nDustin Byfuglien\nDefenseman\n\n$7,600,000  \n\n\n27\n\n\nZach Parise\nLeft Wing\n\n$7,538,462  \n\n\n\n\n\nRyan Suter\nDefenseman\n\n$7,538,462  \n\n\n29\n\n\nAaron Ekblad\nDefenseman\n\n$7,500,000  \n\n\n\n\n\nRyan O\'Reilly\nCenter\n\n$7,500,000  \n\n\n\n\n\nVladimir Tarasenko\nRight Wing\n\n$7,500,000  \n\n\n\n\n\nJason Spezza\nCenter\n\n$7,500,000  \n\n\n33\n\n\nSergei Bobrovsky\nGoaltender\n\n$7,425,000  \n\n\n34\n\n\nMark Stone\nRight Wing\n\n$7,350,000  \n\n\n35\n\n\nKris Letang\nDefenseman\n\n$7,250,000  \n\n\n\n\n\nDavid Krejci\nCenter\n\n$7,250,000  \n\n\n\n\n\nBobby Ryan\nRight Wing\n\n$7,250,000  \n\n\n38\n\n\nJames Van Riemsdyk\nLeft Wing\n\n$7,000,000  \n\n\n\n\n\nPekka Rinne\nGoaltender\n\n$7,000,000  \n\n\n\n\n\nTuukka Rask\nGoaltender\n\n$7,000,000  \n\n\n\n\n\nEvander Kane\nLeft Wing\n\n$7,000,000  \n\n\n\n\n\nMarc-Edouard Vlasic\nDefenseman\n\n$7,000,000  \n\n\n\n\n\nDion Phaneuf\nDefenseman\n\n$7,000,000  \n\n\n\n\n\nDrew Doughty\nDefenseman\n\n$7,000,000  \n\n\n45\n\n\nBrent Seabrook\nDefenseman\n\n$6,875,000  \n\n\n\n\n\nPatrice Bergeron\nCenter\n\n$6,875,000  \n\n\n\n\n\nRyan Kesler\nCenter\n\n$6,875,000  \n\n\n48\n\n\nPhilip Kessel\nRight Wing\n\n$6,800,000  \n\n\n49\n\n\nJohnny Gaudreau\nLeft Wing\n\n$6,750,000  \n\n\n\n\n\nMark Giordano\nDefenseman\n\n$6,750,000  \n\n\n51\n\n\nNicklas Backstrom\nCenter\n\n$6,700,000  \n\n\n52\n\n\nDavid Pastrnak\nRight Wing\n\n$6,666,666  \n\n\n53\n\n\nKevin Shattenkirk\nDefenseman\n\n$6,650,000  \n\n\n54\n\n\nDerek Stepan\nCenter\n\n$6,500,000  \n\n\n\n\n\nPaul Stastny\nCenter\n\n$6,500,000  \n\n\n\n\n\nErik Karlsson\nDefenseman\n\n$6,500,000  \n\n\n\n\n\nAlex Pietrangelo\nDefenseman\n\n$6,500,000  \n\n\n\n\n\nCam Fowler\nDefenseman\n\n$6,500,000  \n\n\n59\n\n\nSean Monahan\nCenter\n\n$6,375,000  \n\n\n60\n\n\nKeith Yandle\nDefenseman\n\n$6,350,000  \n\n\n61\n\n\nNathan MacKinnon\nCenter\n\n$6,300,000  \n\n\n62\n\n\nPatrick Marleau\nLeft Wing\n\n$6,250,000  \n\n\n\n\n\nIlya Kovalchuk\nLeft Wing\n\n$6,250,000  \n\n\n\n\n\nAlexander Radulov\nRight Wing\n\n$6,250,000  \n\n\n65\n\n\nConnor Hellebuyck\nGoaltender\n\n$6,166,666  \n\n\n66\n\n\nBrad Marchand\nLeft Wing\n\n$6,125,000  \n\n\n\n\n\nMark Scheifele\nCenter\n\n$6,125,000  \n\n\n68\n\n\nBraden Holtby\nGoaltender\n\n$6,100,000  \n\n\n\n\n\nDylan Larkin\nCenter\n\n$6,100,000  \n\n\n70\n\n\nHenrik Zetterberg\nLeft Wing\n\n$6,083,333  \n\n\n71\n\n\nFilip Forsberg\nCenter\n\n$6,000,000  \n\n\n\n\n\nKyle Turris\nCenter\n\n$6,000,000  \n\n\n\n\n\nMilan Lucic\nLeft Wing\n\n$6,000,000  \n\n\n\n\n\nRyan Nugent-Hopkins\nCenter\n\n$6,000,000  \n\n\n\n\n\nBrandon Saad\nLeft Wing\n\n$6,000,000  \n\n\n\n\n\nCorey Crawford\nGoaltender\n\n$6,000,000  \n\n\n\n\n\nLoui Eriksson\nRight Wing\n\n$6,000,000  \n\n\n\n\n\nTaylor Hall\nLeft Wing\n\n$6,000,000  \n\n\n\n\n\nCory Schneider\nGoaltender\n\n$6,000,000  \n\n\n\n\n\nErik Johnson\nDefenseman\n\n$6,000,000  \n\n\n\n\n\nDavid Backes\nCenter\n\n$6,000,000  \n\n\n\n\n\nJohnny Boychuk\nDefenseman\n\n$6,000,000  \n\n\n\n\n\nJordan Eberle\nRight Wing\n\n$6,000,000  \n\n\n\n\n\nLogan Couture\nCenter\n\n$6,000,000  \n\n\n\n\n\nJoe Pavelski\nLeft Wing\n\n$6,000,000  \n\n\n\n\n\nArtemi Panarin\nLeft Wing\n\n$6,000,000  \n\n\n\n\n\nKyle Okposo\nRight Wing\n\n$6,000,000  \n\n\n\n\n\nMathew Dumba\nDefenseman\n\n$6,000,000  \n\n\n\n\n\nNikolaj Ehlers\nLeft Wing\n\n$6,000,000  \n\n\n\n\n\nMatt Duchene\nCenter\n\n$6,000,000  \n\n\n\n\n\nJordan Staal\nCenter\n\n$6,000,000  \n\n\n92\n\n\nAleksander Barkov\nCenter\n\n$5,900,000  \n\n\n\n\n\nSemyon Varlamov\nGoaltender\n\n$5,900,000  \n\n\n\n\n\nJonathan Huberdeau\nLeft Wing\n\n$5,900,000  \n\n\n95\n\n\nDustin Brown\nRight Wing\n\n$5,875,000  \n\n\n\n\n\nCameron Atkinson\nRight Wing\n\n$5,875,000  \n\n\n97\n\n\nBrandon Dubinsky\nCenter\n\n$5,850,000  \n\n\n98\n\n\nJonathan Quick\nGoaltender\n\n$5,800,000  \n\n\n\n\n\nRyan Callahan\nRight Wing\n\n$5,800,000  \n\n\n100\n\n\nTravis Zajac\nCenter\n\n$5,750,000  \n\n\n \n\n\t\t\t\t\t\t\xa0\n\t\t\t\t\t\n\n\n\n\n\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\n\nTrending Players \n\n\n\n\n\n\nview\n1\nRick DiPietro (G, NYI)\n\n\nview\n2\nJordin Tootoo (RW, CHI)\n\n\nview\n3\nSidney Crosby (C, PIT)\n\n\nview\n4\nJacob Trouba (D, WPG)\n\n\nview\n5\nDavid Clarkson (RW, VGK)\n\n\n\n\n\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\n\nTrending Pages  \n\n\n\n\n\n\nview\n1\nNHL Homepage\n\n\nview\n2\n NHL Transactions\n\n\nview\n3\n2019 NHL Free Agents\n\n\nview\n4\n2019 NHL Free Agents - Defenseman\n\n\nview\n5\n2018 NHL Cap Hit Rankings\n\n\n\n \n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\n(adsbygoogle = window.adsbygoogle || []).push({});\n\n\n\n\n\n\nMore NHL Trackers \n\n\n\n\n\n\nview\nTeam Cap Tracker\n\n\nview\nFree Agent Tracker\n\n\nview\nFines & Suspensions\n\n\nview\nTrade Tracker\n\n\nview\nBuyout Calculator\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\nNFL\nNBA\nMLB\nNHL\nMLS\nEPL\nResearch\nBlog\nBack to top \n\n\n\n\n\n\n\n\n\n\n\nAbout Spotrac\nBrowse our online application for MLB, NBA, NFL, NHL, EPL, or MLS player contracts, salaries, transactions, and more.\n\n\n\n\n\n\nAdditional information\n\nAbout Spotrac\nMedia Requests\nAdvertise with Us\nPrivacy Policy\nGoogle Privacy\nContact Us\nSources & Affiliates\n\n\n\n\n\n\n\n© Spotrac 2018. All rights reserved.\n\n\nPartnered with the USA TODAY Sports Media Group\xa0\xa0|\xa0\xa0Powered by MG3 WebWorks.\n\n\n\n\n\n\n\n\nfunction openCity(evt, cityName) {\n    var i, tabcontent, tablinks;\n    tabcontent = document.getElementsByClassName("tabcontent");\n    for (i = 0; i < tabcontent.length; i++) {\n        tabcontent[i].style.display = "none";\n    }\n    tablinks = document.getElementsByClassName("tablinks");\n    for (i = 0; i < tablinks.length; i++) {\n        tablinks[i].className = tablinks[i].className.replace(" active", "");\n    }\n    document.getElementById(cityName).style.display = "block";\n    evt.currentTarget.className += " active";\n}\n\n// Get the element with id="defaultOpen" and click on it\ndocument.getElementById("defaultOpen").click();\n\n\n  (function(i,s,o,g,r,a,m){i[\'GoogleAnalyticsObject\']=r;i[r]=i[r]||function(){\n  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),\n  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)\n  })(window,document,\'script\',\'//www.google-analytics.com/analytics.js\',\'ga\');\n\n  ga(\'create\', \'UA-4232275-1\', \'auto\');\n  ga(\'require\', \'linkid\', \'linkid.js\');\n  ga(\'send\', \'pageview\');\n\n\n\n\n  _ga.trackTwitter(); \n  _ga.trackFacebook();//Google Analytics tracking\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nvar ajax = "rankings";\nvar ajaxUrl = "https://www.spotrac.com/nhl/rankings/cap-hit/";\n\n\n\n\n$("#positionUrl").change(function(){\n\t$("#defenseUrlWrapper").hide();\n\t$("#defenseUrl").val("");\n\t\t\n\tif($("#positionUrl option:selected").attr("data-category")=="defense" || $(this).val()=="defense")\n\t{\n\t\t$("#defenseUrlWrapper").show();\t\n\t}\n});\n\n'

# To pull out the player_name and cap_hit
player_name = soup.find_all('a', {'class':'team-name'})
cap_hit = soup.find_all('span', {'class':'info'})
# Used this for year at one point, decided to scrap it to generate within the below loop
for_year = soup.find_all('h2', {'style':'border-bottom:0px;margin-bottom:0px;padding-bottom:0px;'})

# Check to make sure pulled correctly
print(f'''
{player_name[9].text},
{cap_hit[9].text},
{for_year[0].text[:4]}
''')

Jamie Benn,
$9,500,000  ,
2018

# Test out the first pull to make sure I'm doing this correctly
test_url = "https://www.spotrac.com/nhl/rankings/2011/cap-hit/anaheim-ducks"
res = requests.get(test_url)
soup = BeautifulSoup(res.content, 'lxml')
player_name = soup.find_all('a', {'class':'team-name'})
cap_hit = soup.find_all('span', {'class':'info'})

# Test print
print(f'''
{player_name[0].text},
{cap_hit[0].text}
''')

Ryan Getzlaf,
$5,325,000  

After further testing, it doesn’t seem that above URL can pull more than 100 entries at a time.
Furthermore, the page uses ‘never-ending’ scrolling, so the URL never changes once you get past the first 100 entries.
I’m going to have to create a loop to loop through each year, and I’m going to do it by team name.
Max amount of contracts an NHL team can have is 50, so this should hopefully work.
As a result, I’ll have to create a list with each team name in it.

Note: The number of teams in this list does not equal the number of teams that have been in the NHL from 2011 to 2017. The Arizona Coyotes were named the Phoenix Coyotes up until 2014 (so they appear twice), and the Vegas Golden Knights are an expansion team that was added in 2017.

teams = ['anaheim-ducks',
         'arizona-coyotes',
         'boston-bruins',
         'buffalo-sabres',
         'calgary-flames',
         'carolina-hurricanes',
         'chicago-blackhawks',
         'colorado-avalanche',
         'columbus-blue-jackets',
         'dallas-stars',
         'detroit-red-wings',
         'edmonton-oilers',
         'florida-panthers',
         'los-angeles-kings',
         'minnesota-wild',
         'montreal-canadiens',
         'nashville-predators',
         'new-jersey-devils',
         'new-york-islanders',
         'new-york-rangers',
         'ottawa-senators',
         'philadelphia-flyers',
         'phoenix-coyotes',
         'pittsburgh-penguins',
         'san-jose-sharks',
         'st-louis-blues',
         'tampa-bay-lightning',
         'toronto-maple-leafs',
         'vancouver-canucks',
         'vegas-golden-knights',
         'washington-capitals',
         'winnipeg-jets']

# Make sure this loop will work for all the teams
for team in teams:
    print("team_name:{}".format(team))

team_name:anaheim-ducks
team_name:arizona-coyotes
team_name:boston-bruins
team_name:buffalo-sabres
team_name:calgary-flames
team_name:carolina-hurricanes
team_name:chicago-blackhawks
team_name:colorado-avalanche
team_name:columbus-blue-jackets
team_name:dallas-stars
team_name:detroit-red-wings
team_name:edmonton-oilers
team_name:florida-panthers
team_name:los-angeles-kings
team_name:minnesota-wild
team_name:montreal-canadiens
team_name:nashville-predators
team_name:new-jersey-devils
team_name:new-york-islanders
team_name:new-york-rangers
team_name:ottawa-senators
team_name:philadelphia-flyers
team_name:phoenix-coyotes
team_name:pittsburgh-penguins
team_name:san-jose-sharks
team_name:st-louis-blues
team_name:tampa-bay-lightning
team_name:toronto-maple-leafs
team_name:vancouver-canucks
team_name:vegas-golden-knights
team_name:washington-capitals
team_name:winnipeg-jets

# For testing, and clearing out my data when I need to
df_cap = pd.DataFrame()

# Scraper for salary cap info
# Create empty dataframe
df_cap = pd.DataFrame()
# Begin loop from teams created earlier
for team in teams:
    # The website I'm pulling from only goes back to 2011
    # And I'm not going to be using data from the still-very-young 2018-19 season, so range is 2011 to 2017
    for i in range(1,8):

        # Generate URL
        url_base_cap_hit = ("https://www.spotrac.com/nhl/rankings/201{}/cap-hit/{}".format(i, team))

        # Request get
        res = requests.get(url_base_cap_hit)

        # Create soup object
        soup = BeautifulSoup(res.content, 'lxml')

        # Pull out relevant information
        player_name = soup.find_all('a', {'class':'team-name'})
        cap_hit = soup.find_all('span', {'class':'info'})

        # Append new data to DF
        for a in range(0, len(player_name)):
            df_cap = df_cap.append({'player_name': player_name[a].text,
                                          'cap_hit': cap_hit[a].text,
                                          'team_name':team,
                                          'year':"201{}".format(i)}, ignore_index = True)

# I saved the CSV originally, but so much data cleaning occurs afterwards
# that I'd just prefer to resave after cleaning down the road
# df_cap.to_csv('cap_data_uncleaned.csv', index=True)

# Previously saved
# df_cap.to_csv('./data/cap_data.csv', index=True)

# For when I want to start fresh with my uncleaned data - leaving commented out for nwo
# df_cap = pd.read_csv('./data/cap_data_uncleaned.csv', index_col='Unnamed: 0')

# Get rid of the extra index
# df_cap = df_cap.drop(labels='Unnamed: 0.1', axis=1)

# Checking that the shape makes sense
df_cap.shape

(6485, 4)

# I had an issue with my loop that was causing players to appear on the wrong team
# This is just to check using similarly named players
df_cap[df_cap['player_name'].str.contains('Rask') == True]

	cap_hit	player_name	team_name	year
382	$1,250,000	Tuukka Rask	boston-bruins	2011
406	$3,500,000	Tuukka Rask	boston-bruins	2012
427	$7,000,000	Tuukka Rask	boston-bruins	2013
456	$7,000,000	Tuukka Rask	boston-bruins	2014
479	$7,000,000	Tuukka Rask	boston-bruins	2015
514	$7,000,000	Tuukka Rask	boston-bruins	2016
550	$7,000,000	Tuukka Rask	boston-bruins	2017
1124	$763,333	Victor Rask	carolina-hurricanes	2014
1156	$680,833	Victor Rask	carolina-hurricanes	2015
1170	$4,000,000	Victor Rask	carolina-hurricanes	2016
1204	$4,000,000	Victor Rask	carolina-hurricanes	2017

# Check out the types
# cap_hit will need to be converted to a int
df_cap.dtypes

cap_hit        object
player_name    object
team_name      object
year           object
dtype: object

# I'll need to get rid of the dollar sign, the commas, and the blank spaces in the cap hit column
df_cap['cap_hit'] = df_cap['cap_hit'].map(lambda x: x.replace('$',''))
df_cap['cap_hit'] = df_cap['cap_hit'].map(lambda x: x.replace(',',''))
df_cap['cap_hit'] = df_cap['cap_hit'].map(lambda x: x.replace('  ',''))

df_cap['cap_hit'] = df_cap['cap_hit'].astype(int)

df_cap.dtypes

cap_hit         int64
player_name    object
team_name      object
year           object
dtype: object

# Need to convert year back to object for changing to season
df_cap['year'] = df_cap['year'].astype(object)

df_cap.dtypes

cap_hit         int64
player_name    object
team_name      object
year           object
dtype: object

# Confirming on the frontend that these values match
df_cap.head()

	cap_hit	player_name	team_name	year
0	5325000	Ryan Getzlaf	anaheim-ducks	2011
1	5325000	Corey Perry	anaheim-ducks	2011
2	5100000	Bobby Ryan	anaheim-ducks	2011
3	4500000	Jonas Hiller	anaheim-ducks	2011
4	4000000	Teemu Selanne	anaheim-ducks	2011

# Confirming on the frontend that these values match
df_cap.tail()

	cap_hit	player_name	team_name	year
6480	675000	Jamie Phillips	winnipeg-jets	2017
6481	650000	Joe Morrow	winnipeg-jets	2017
6482	650000	Michael Sgarbossa	winnipeg-jets	2017
6483	636666	Eric Comrie	winnipeg-jets	2017
6484	625000	Julian Melchiori	winnipeg-jets	2017

# pd.set_option('display.max_row', 100)
# df_cap

Cleaning up the Cap Hit Data

This data will need cleaned up in a few different ways so that it can be merged with the main player performance data.

# For later usage
S2Y_converter = {"2011-2012":"2011",
                  "2012-2013":"2012",
                  "2013-2014":"2013",
                  "2014-2015":"2014",
                  "2015-2016":"2015",
                  "2016-2017":"2016",
                  "2017-2018":"2017"}

Y2S_converter = {"2011":"2011-2012",
                  "2012":"2012-2013",
                  "2013":"2013-2014",
                  "2014":"2014-2015",
                  "2015":"2015-2016",
                  "2016":"2016-2017",
                  "2017":"2017-2018"}

# Change the data and the label from year to Season
df_cap['year'] = df_cap['year'].map(lambda x: x.replace(x, Y2S_converter[x]))

# For merging the dataframes down the line
df_cap.rename(columns={'player_name':'Player', 'year':'Season'}, inplace=True)

# Fix any potential oddities in the name column, so the merges work better
df_cap['Player'] = df_cap['Player'].map(lambda x: x.title())

df_cap.head()

	cap_hit	Player	team_name	Season
0	5325000	Ryan Getzlaf	anaheim-ducks	2011-2012
1	5325000	Corey Perry	anaheim-ducks	2011-2012
2	5100000	Bobby Ryan	anaheim-ducks	2011-2012
3	4500000	Jonas Hiller	anaheim-ducks	2011-2012
4	4000000	Teemu Selanne	anaheim-ducks	2011-2012

# To be able to merge the data, I need to be able to match on name
# So this is to simplify common names to their most base form
# Lemmatization for names!
Name_simplifier = {'Thomas':'Tom',
                   'Alexander':'Alex',
                   'Alexandre':'Alex',
                   'Alexey':'Alex',
                   'Alexei':'Alex',
                   'Christopher':'Chris',
                   'Christian':'Chris',
                   'Cameron':'Cam',
                   'Danny':'Dan',
                   'Daniel':'Dan',
                   'T.J.':'Tj',
                   'T J ':'Tj',
                   'T J':'Tj',
                   'Vaclav':'Vincent',
                   'Vinnie':'Vincent',
                   'Zachary':'Zach',
                   'Zac':'Zach',
                   'Zack':'Zach'}

Name_simplifier['Thomas']

'Tom'

list(Name_simplifier.keys())

['Thomas',
 'Alexander',
 'Alexandre',
 'Alexey',
 'Alexei',
 'Christopher',
 'Christian',
 'Cameron',
 'Danny',
 'Daniel',
 'T.J.',
 'T J ',
 'T J',
 'Vaclav',
 'Vinnie',
 'Zachary',
 'Zac',
 'Zack']

# Example of a player whose name will need changed
df_cap[df_cap['Player'] == 'Alexander Semin']

	cap_hit	Player	team_name	Season
1063	7000000	Alexander Semin	carolina-hurricanes	2012-2013
1087	7000000	Alexander Semin	carolina-hurricanes	2013-2014
1112	7000000	Alexander Semin	carolina-hurricanes	2014-2015
6074	6700000	Alexander Semin	washington-capitals	2011-2012

# I get by with a little help from my TAs
# This is to apply the name changes for merging on later
df_cap['Player'] = df_cap['Player'].map(
    lambda x: x.split(' ')[0].replace(x.split(' ')[0], Name_simplifier[x.split(' ')[0]]) + ' ' + x.split(' ')[1]
    if x.split(' ')[0] in Name_simplifier.keys() else x)

# Check to make sure it worked
df_cap[df_cap['Player'].str.contains('Semin') == True]

	cap_hit	Player	team_name	Season
1063	7000000	Alex Semin	carolina-hurricanes	2012-2013
1087	7000000	Alex Semin	carolina-hurricanes	2013-2014
1112	7000000	Alex Semin	carolina-hurricanes	2014-2015
6074	6700000	Alex Semin	washington-capitals	2011-2012

# df_cap.to_csv('./data/cap_data_cleaned.csv', index=True)

Data Cleaning & Combining

Part I - Combining player performance and cap hit

I’ll be using the player performance data that I pulled from http://corsica.hockey/skater-stats/, team data from http://corsica.hockey/team-stats/, and the cap data I scrapped in Part II. This next section I’ll work on cleaning the data up, and combining these three data sets. Before that however, I’d like to highlight a important distinction between the data I’m looking at.

Explaining the different play situations

In an normal NHL game, teams usually play each other with 5 skaters on the ice (3 forwards + 2 defensemen) and a goalie. This is known as ‘even strength’ play. However, a team can be assessed a penalty, which is an infraction for breaking a rule. The penalized team is forced to play down a man for either 2, 4, or 5 minutes, depending on the severity of the penalty. By far the most common penalty is 2 minutes though. During the ensuing 5 on 4, known as being on the ‘powerplay,’ play opens up significantly, and generally the team that is up a man is able to control the puck better because they have more open ice to skate, and passing lanes to distribute the puck to teammates.

The key takeaway is that the powerplay is a distinct type of gameplay, and is played in a different style than normal 5 on 5 hockey. Coaches mix up how they deploy players, and players play different positions than they would normally. For example, often coaches will play 4 forwards and 1 defensemen on the powerplay.

I highlight this fact because I will be making a distinction between stats collected at even strength play versus those on the powerplay because of how different of a play style they both are.

All that being said, I may stick with just one set of data over another for sake of streamlining the project. Studying subject-matter material may reveal something I don’t understand about the data. If I do go that direction, it will certainly be a subject I explore further post-DSI.

%pwd

'/Users/tomkelly/Desktop/general_assembly/DSI-US-5/Capstone-Project'

# All game situations (even strength + powerplay)
df_all = pd.read_csv('./data/skaters_All_Data.csv')
# Just even strength
df_es = pd.read_csv('./data/skaters_ES_Data.csv')
# Team data
df_team = pd.read_csv('./data/team_stats.csv')

print(f'''
{df_all.shape},
{df_es.shape},
{df_team.shape}
''')

(4746, 44),
(4676, 44),
(211, 28)

df_all.head()

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	ixGF	ixGF/60	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC
0	5EBASTIAN.AHO	2017-2018	NYI	D/D/R	22	357.43	1	3	4	2	...	0.90	0.15	4.17	100.13	44.69	27.12	28.19	49.83	29.49	48.01
1	AARON.EKBLAD	2014-2015	FLA	D	81	1766.60	12	27	39	22	...	9.11	0.31	7.06	100.62	66.19	35.68	30.40	53.99	30.17	46.05
2	AARON.EKBLAD	2015-2016	FLA	D	78	1690.83	15	20	35	23	...	9.81	0.35	8.24	101.92	60.39	35.80	31.18	52.08	30.95	47.13
3	AARON.EKBLAD	2016-2017	FLA	D	68	1459.29	10	11	21	14	...	12.02	0.49	4.44	97.21	64.51	35.12	30.87	54.64	30.87	47.02
4	AARON.EKBLAD	2017-2018	FLA	D	82	1917.89	16	22	38	22	...	12.96	0.41	8.47	101.72	41.08	38.65	33.37	52.47	33.19	51.10

5 rows × 44 columns

As a self-considered subject matter expert, I’m already aware that ‘Sebastian Aho’ is the only name that is used by two seperate NHL players. Furthermore, one of them has played only 22 NHL games total and is now in the minor leagues, where the other has played over 180 and counting and is a staple on his team (Carolina Hurricanes). As I’ll be combining cap data on name, I’ll need to remove this potential duplicate issue. And since the former Sebastian Aho has significantly less NHL games under his belt, I’m going to remove his data.

# Dropping the 'other' Sebastian Aho and resetting the index
df_all = df_all.drop(index=0, axis=0).reset_index(drop=True)
# df_es = df_es.drop(index=0, axis=0).reset_index(drop=True)

# Quick check to make sure it worked
df_all.head()

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	ixGF	ixGF/60	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC
0	AARON.EKBLAD	2014-2015	FLA	D	81	1766.60	12	27	39	22	...	9.11	0.31	7.06	100.62	66.19	35.68	30.40	53.99	30.17	46.05
1	AARON.EKBLAD	2015-2016	FLA	D	78	1690.83	15	20	35	23	...	9.81	0.35	8.24	101.92	60.39	35.80	31.18	52.08	30.95	47.13
2	AARON.EKBLAD	2016-2017	FLA	D	68	1459.29	10	11	21	14	...	12.02	0.49	4.44	97.21	64.51	35.12	30.87	54.64	30.87	47.02
3	AARON.EKBLAD	2017-2018	FLA	D	82	1917.89	16	22	38	22	...	12.96	0.41	8.47	101.72	41.08	38.65	33.37	52.47	33.19	51.10
4	AARON.JOHNSON	2011-2012	CBJ	D	56	924.40	3	13	16	9	...	3.01	0.20	4.76	98.13	41.93	27.55	27.78	47.36	30.73	51.72

5 rows × 44 columns

# Looks like the player name needs cleaned up a bit
# Replace the period
df_all['Player'] = df_all['Player'].map(lambda x: x.replace('.', ' '))
# df_es['Player'] = df_es['Player'].map(lambda x: x.replace('.', ' '))

# Use .title() to match the casing in the other data set
df_all['Player'] = df_all['Player'].map(lambda x: x.title())
# df_es['Player'] = df_es['Player'].map(lambda x: x.title())

# Check to make sure these worked
df_all['Player'].head(10)

    Aaron Ekblad
    Aaron Ekblad
    Aaron Ekblad
    Aaron Ekblad
   Aaron Johnson
      Aaron Ness
  Aaron Palushaj
  Aaron Palushaj
      Aaron Rome
      Aaron Rome
Name: Player, dtype: object

# Taking a look at the teams to determine if I need to drop some data points
df_all['Team'].unique()

array(['FLA', 'CBJ', 'NYI', 'MTL', 'COL', 'VAN', 'DAL', 'VAN/WSH', 'WSH',
       'S.J', 'CHI/VAN', 'PIT/EDM', 'NYR', 'VAN/EDM', 'T.B',
       'T.B/PHI/CAR', 'PHI', 'N.J', 'N.J/ANA', 'EDM', 'WPG', 'BOS', 'BUF',
       'EDM/WPG', 'ARI', 'L.A', 'EDM/OTT', 'WPG/ARI', 'VAN/OTT', 'OTT',
       'CGY', 'CHI', 'NSH', 'N.J/CAR', 'N.J/WPG', 'DET', 'DET/TOR', 'PIT',
       'STL', 'CAR', 'BUF/VAN', 'COL/ARI', 'VGK', 'TOR', 'COL/MTL',
       'CAR/PHI', 'MTL/NSH', 'PHI/BOS', 'CAR/DET', 'CAR/L.A', 'COL/CBJ',
       'ANA', 'S.J/CHI', 'WPG/CHI', 'NYI/PHI', 'ARI/CHI', 'CBJ/ARI',
       'ANA/PIT', 'CHI/S.J', 'TOR/S.J', 'TOR/COL', 'T.B/CGY', 'CGY/NYI',
       'CGY/CBJ', 'NSH/MTL', 'NSH/N.J', 'N.J/PIT', 'MIN/MTL', 'EDM/MTL',
       'MTL/EDM/NYI', 'ARI/VAN', 'CHI/FLA', 'FLA/ANA', 'NSH/COL',
       'PHI/T.B', 'VGK/VAN', 'CGY/CHI', 'DET/NYR', 'DAL/S.J', 'DAL/PIT',
       'T.B/BOS', 'T.B/TOR', 'BUF/MTL', 'OTT/T.B', 'NYI/BOS', 'WSH/TOR',
       'MIN', 'ARI/PIT/NSH', 'STL/BUF', 'BUF/MIN', 'MIN/CGY', 'ARI/NYR',
       'L.A/CHI', 'COL/ANA', 'ANA/NYI', 'TOR/NSH', 'VAN/BUF', 'MIN/BUF',
       'COL/NSH', 'NSH/NYR', 'TOR/OTT', 'MIN/CBJ', 'TOR/WSH', 'BOS/EDM',
       'CBJ/BUF', 'T.B/OTT', 'OTT/BUF', 'BOS/ARI', 'CGY/WSH', 'OTT/CGY',
       'VAN/MTL', 'MTL/CHI', 'CBJ/N.J', 'L.A/NYR', 'COL/S.J', 'TOR/PIT',
       'NYR/MIN', 'TOR/CBJ', 'VAN/FLA', 'MTL/EDM', 'CGY/MIN', 'NSH/DET',
       'EDM/PIT', 'PIT/ANA', 'OTT/ARI', 'ARI/CGY/DAL', 'TOR/ANA',
       'PIT/N.J', 'L.A/MTL', 'BUF/NSH', 'DAL/VAN', 'NSH/EDM', 'CBJ/NYR',
       'OTT/PIT', 'ANA/MTL', 'MTL/N.J', 'OTT/L.A', 'T.B/S.J', 'S.J/PIT',
       'DET/EDM', 'BUF/WPG', 'BOS/WPG', 'ANA/WSH', 'NYR/VAN',
       'T.B/TOR/ANA', 'PIT/TOR', 'S.J/TOR', 'N.J/COL', 'CAR/NYR',
       'WPG/PIT', 'MTL/DAL', 'DAL/DET', 'BUF/S.J', 'BOS/FLA', 'CBJ/WPG',
       'FLA/T.B', 'MIN/BOS', 'STL/PIT', 'PIT/CBJ', 'WSH/CAR', 'L.A/CBJ',
       'MTL/WSH', 'DET/FLA', 'S.J/NYR', 'CBJ/ANA', 'S.J/COL', 'BUF/ANA',
       'VAN/S.J', 'CGY/PIT', 'COL/L.A', 'DAL/BOS', 'N.J/FLA', 'CAR/MTL',
       'NYI/ANA', 'S.J/DAL', 'CGY/STL', 'CAR/WPG', 'CBJ/L.A', 'NYR/MTL',
       'CBJ/CHI', 'NSH/FLA', 'FLA/EDM', 'FLA/CHI', 'CGY/FLA', 'MTL/ANA',
       'ARI/ANA/CHI', 'STL/TOR', 'MTL/WPG', 'CAR/TOR', 'CAR/BOS',
       'NYR/ARI', 'DAL/CHI', 'OTT/PHI', 'CHI/NYR', 'COL/BOS', 'BUF/STL',
       'CBJ/MIN/STL', 'DAL/MTL', 'ANA/VAN', 'PIT/ARI', 'CAR/PIT',
       'ANA/T.B', 'NYR/T.B', 'EDM/CBJ/VAN/L.A', 'DAL/CGY', 'CGY/OTT',
       'OTT/BOS', 'L.A/FLA', 'TOR/T.B', 'DET/PHI', 'CBJ/DAL', 'STL/WSH',
       'CGY/CAR', 'CBJ/STL', 'CGY/DAL', 'FLA/DAL', 'ANA/N.J/MIN',
       'COL/DET', 'N.J/CBJ', 'ARI/OTT', 'OTT/NSH', 'EDM/CGY', 'DET/BOS',
       'DAL/CBJ', 'NYR/WPG', 'N.J/BOS', 'PHI/L.A', 'STL/OTT', 'T.B/CAR',
       'FLA/PIT', 'FLA/VAN', 'MIN/N.J', 'N.J/DET', 'NYR/CBJ', 'L.A/OTT',
       'EDM/PIT/ARI/NSH', 'MTL/COL', 'TOR/EDM', 'EDM/CBJ', 'CHI/WPG',
       'PHI/PIT', 'CGY/VAN', 'NSH/WSH', 'WSH/ARI', 'ARI/MIN', 'T.B/NYR',
       'STL/N.J', 'PIT/BUF', 'COL/OTT', 'ARI/ANA', 'BUF/MIN/NYI',
       'OTT/NYR', 'COL/PHI', 'NYR/NSH', 'NYR/N.J', 'CHI/T.B', 'ARI/CGY',
       'CHI/WSH', 'EDM/TOR', 'S.J/EDM', 'S.J/MTL', 'MTL/CGY', 'L.A/EDM',
       'T.B/DET', 'MTL/MIN', 'MIN/NYR', 'FLA/WPG', 'FLA/MTL', 'BUF/WSH',
       'ARI/COL', 'OTT/TOR', 'MIN/STL', 'VAN/L.A', 'NYR/BOS', 'DAL/PHI',
       'N.J/MIN', 'MIN/EDM', 'L.A/OTT/CGY', 'T.B/MTL', 'CGY/ANA',
       'NSH/TOR/STL', 'N.J/NSH', 'DET/NSH', 'DAL/ANA', 'ANA/EDM',
       'EDM/N.J', 'STL/WPG', 'T.B/PHI', 'ANA/TOR', 'TOR/ARI', 'NYI/CHI',
       'MTL/ARI', 'CHI/MTL', 'EDM/NSH', 'ARI/S.J', 'MTL/VAN/NYR',
       'N.J/VAN/NSH', 'CGY/MTL', 'MTL/ANA/CBJ', 'CHI/ARI', 'DET/PIT',
       'PIT/STL', 'ARI/EDM/PIT', 'BOS/NYR', 'PIT/CHI/L.A', 'BUF/L.A',
       'ANA/N.J', 'S.J/VGK', 'NYR/S.J', 'CHI/ANA', 'CHI/NSH', 'CBJ/NSH',
       'COL/TOR', 'PIT/VGK', 'CBJ/VAN', 'NSH/S.J', 'BUF/DET/PIT',
       'FLA/MIN', 'TOR/FLA', 'NSH/CBJ', 'OTT/NYI', 'L.A/PHI', 'ARI/NSH',
       'N.J/MTL', 'T.B/COL', 'PHI/COL', 'DET/MTL', 'ARI/N.J', 'WSH/CBJ',
       'NYR/PIT', 'EDM/FLA', 'NYI/MTL/BUF', 'VAN/CBJ', 'CBJ/CHI/TOR',
       'CAR/WSH', 'NYI/T.B', 'FLA/BUF', 'ARI/L.A', 'DET/CHI', 'MTL/TOR',
       'DET/VGK', 'EDM/MIN', 'S.J/OTT', 'CHI/BOS', 'CHI/PIT', 'CAR/N.J',
       'S.J/ARI', 'S.J/NYI', 'CAR/OTT', 'STL/BOS', 'NYR/FLA', 'WPG/BUF',
       'WSH/STL', 'ARI/STL'], dtype=object)

df_all[df_all['Team'].str.contains('/') == True].shape

(422, 44)

So that’s 422 data points out of roughly 4700 that are on a player traded mid-season. I’m going to drop these rows as it complicates the data and analysis. Players traded mid-season may perform drastically different team-to-team.

# There's probably a cleaner way to do this, but this works:
df_all.drop(index=df_all[df_all['Team'].str.contains('/') == True].index, axis=0, inplace=True)
# df_es.drop(index=df_es[df_es['Team'].str.contains('/') == True].index, axis=0, inplace=True)

# Check
df_all[df_all['Team'].str.contains('/') == True]

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	ixGF	ixGF/60	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC

0 rows × 44 columns

# This is to apply the name changes on df_all
df_all['Player'] = df_all['Player'].map(
    lambda x: x.split(' ')[0].replace(x.split(' ')[0], Name_simplifier[x.split(' ')[0]]) + ' ' + x.split(' ')[1]
    if x.split(' ')[0] in Name_simplifier.keys() else x)

# # This is to apply the name changes on df_es
# df_es['Player'] = df_es['Player'].map(
#     lambda x: x.split(' ')[0].replace(x.split(' ')[0], Name_simplifier[x.split(' ')[0]]) + ' ' + x.split(' ')[1]
#     if x.split(' ')[0] in Name_simplifier.keys() else x)

# Check to make sure it worked
df_all[df_all['Player'].str.contains('Oshie') == True]

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	ixGF	ixGF/60	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC
4335	Tj Oshie	2011-2012	STL	R	80	1562.33	19	35	54	43	...	21.15	0.81	10.11	101.75	50.65	32.05	34.410000	54.64	33.100000	49.77
4336	Tj Oshie	2012-2013	STL	R	30	572.80	7	12	19	14	...	6.35	0.67	10.77	95.95	51.45	31.44	33.980000	54.21	32.480000	48.98
4337	Tj Oshie	2013-2014	STL	R	79	1499.74	21	38	59	47	...	18.70	0.75	13.82	102.91	56.08	31.14	35.100000	56.48	33.290000	49.32
4338	Tj Oshie	2014-2015	STL	R	72	1356.03	19	36	55	42	...	17.57	0.78	11.73	101.92	51.28	30.94	34.060000	54.23	32.190000	49.12
4339	Tj Oshie	2015-2016	WSH	R	80	1517.09	26	25	51	37	...	20.71	0.82	14.05	103.02	56.70	31.38	inf	56.27	inf	47.99
4340	Tj Oshie	2016-2017	WSH	R	68	1214.12	33	23	56	49	...	20.07	0.99	23.08	106.08	59.57	29.44	37.090000	55.64	32.770000	47.26
4341	Tj Oshie	2017-2018	WSH	R	74	1362.54	19	28	47	36	...	15.57	0.69	14.84	101.97	60.40	30.50	40.480000	53.99	32.610000	45.83

7 rows × 44 columns

df_all_cap = pd.merge(df_all, df_cap, how='left', left_on=['Player','Season'], right_on=['Player','Season'])
# df_es_cap = pd.merge(df_es, df_cap, how='left', left_on=['Player','Season'], right_on=['Player','Season'])

df_all_cap.head(10)

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC	cap_hit	team_name
0	Aaron Ekblad	2014-2015	FLA	D	81	1766.60	12	27	39	22	...	7.06	100.62	66.19	35.68	30.40	53.99	30.17	46.05	1775000.0	florida-panthers
1	Aaron Ekblad	2015-2016	FLA	D	78	1690.83	15	20	35	23	...	8.24	101.92	60.39	35.80	31.18	52.08	30.95	47.13	925000.0	florida-panthers
2	Aaron Ekblad	2016-2017	FLA	D	68	1459.29	10	11	21	14	...	4.44	97.21	64.51	35.12	30.87	54.64	30.87	47.02	925000.0	florida-panthers
3	Aaron Ekblad	2017-2018	FLA	D	82	1917.89	16	22	38	22	...	8.47	101.72	41.08	38.65	33.37	52.47	33.19	51.10	7500000.0	florida-panthers
4	Aaron Johnson	2011-2012	CBJ	D	56	924.40	3	13	16	9	...	4.76	98.13	41.93	27.55	27.78	47.36	30.73	51.72	550000.0	columbus-blue-jackets
5	Aaron Ness	2013-2014	NYI	D	20	295.88	1	1	2	1	...	4.35	91.63	50.86	24.39	27.13	48.67	29.44	50.97	NaN	NaN
6	Aaron Palushaj	2011-2012	MTL	R	38	287.52	1	4	5	2	...	2.70	99.93	48.46	13.02	26.89	47.12	27.81	48.81	883333.0	montreal-canadiens
7	Aaron Palushaj	2012-2013	COL	R	25	282.90	1	7	8	4	...	3.57	98.16	51.35	18.97	30.06	51.16	28.91	47.74	NaN	NaN
8	Aaron Rome	2011-2012	VAN	D	43	654.73	4	5	9	6	...	9.52	97.68	44.78	25.04	27.64	51.91	29.20	50.27	750000.0	vancouver-canucks
9	Aaron Rome	2012-2013	DAL	D	27	414.03	0	5	5	3	...	0.00	99.17	37.82	25.68	27.55	46.18	30.56	53.46	1500000.0	dallas-stars

10 rows × 46 columns

# Looks like I need to go back through my data and figure out why the merge didn't work as well I would have liked
df_all_cap.isnull().sum().sort_values(ascending=False)

team_name    293
cap_hit      293
P1/60          0
GA             0
GF             0
Rel CF%        0
CF%            0
C+/-           0
CA             0
CF             0
GS/60          0
GS             0
P/60           0
P1             0
P              0
A              0
G              0
TOI            0
GP             0
Position       0
Team           0
Season         0
G+/-           0
GF%            0
Rel GF%        0
xGF            0
CF% QoC        0
TOI% QoC       0
CF% QoT        0
TOI% QoT       0
TOI%           0
ZSR            0
PDO            0
iSh%           0
ixGF/60        0
ixGF           0
iCF/60         0
iCF            0
iP+/-          0
iPEND          0
iPENT          0
Rel xGF%       0
xGF%           0
xG+/-          0
xGA            0
Player         0
dtype: int64

df_all_cap[df_all_cap['cap_hit'].isnull() == True]

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC	cap_hit	team_name
5	Aaron Ness	2013-2014	NYI	D	20	295.88	1	1	2	1	...	4.35	91.63	50.86	24.39	27.130000	48.67	29.44	50.97	NaN	NaN
7	Aaron Palushaj	2012-2013	COL	R	25	282.90	1	7	8	4	...	3.57	98.16	51.35	18.97	30.060000	51.16	28.91	47.74	NaN	NaN
15	Adam Burish	2014-2015	S.J	R	20	222.90	1	2	3	3	...	4.55	95.52	23.60	18.53	28.480000	47.00	30.63	54.47	NaN	NaN
20	Adam Hall	2011-2012	T.B	R	57	676.36	2	5	7	6	...	3.17	94.60	21.33	19.80	32.740000	39.91	35.02	59.31	NaN	NaN
81	Alex Burmistrov	2017-2018	VAN	C	23	278.11	2	4	6	5	...	12.50	97.63	44.94	20.11	30.240000	48.29	29.90	50.30	NaN	NaN
108	Alex Marchenko	2014-2015	DET	D	13	200.42	1	1	2	1	...	14.29	99.21	61.32	25.72	27.130000	51.16	29.09	52.37	NaN	NaN
141	Alex Petrovic	2014-2015	FLA	D	33	536.57	0	2	2	0	...	0.00	98.10	51.70	26.87	27.550000	49.77	29.37	51.86	NaN	NaN
145	Alex Picard	2011-2012	PIT	D	17	223.42	0	3	3	2	...	0.00	99.81	67.19	22.20	26.910000	54.99	28.68	49.74	NaN	NaN
173	Alex Urbom	2013-2014	WSH	D	20	292.11	1	1	2	2	...	6.25	102.21	46.97	24.17	27.320000	44.30	30.75	53.91	NaN	NaN
193	Andre Burakovsky	2014-2015	WSH	L	53	684.81	9	13	22	15	...	13.85	103.13	67.20	21.72	31.440000	54.47	29.51	46.66	NaN	NaN
242	Andrew Ebbett	2014-2015	PIT	C	24	216.12	1	4	5	4	...	4.76	101.17	50.00	15.28	27.490000	47.62	30.09	53.26	NaN	NaN
260	Andrew Murray	2011-2012	S.J	C	39	300.48	1	3	4	3	...	3.03	97.97	62.75	13.08	26.280000	49.18	28.27	51.06	NaN	NaN
289	Anthony Duclair	2014-2015	NYR	L	18	218.82	1	6	7	5	...	5.56	104.24	66.95	20.22	30.180000	54.38	29.26	46.13	NaN	NaN
361	Barclay Goodrow	2014-2015	S.J	C	60	664.31	4	8	12	11	...	5.97	100.77	47.55	18.65	inf	50.06	29.00	50.55	NaN	NaN
368	Beau Bennett	2012-2013	PIT	R	26	319.80	3	11	14	7	...	10.00	110.21	64.95	20.74	31.810000	53.86	29.41	45.40	NaN	NaN
396	Bj Crombeen	2011-2012	STL	R	40	331.99	1	2	3	3	...	2.00	98.13	51.68	14.13	26.590000	52.32	27.91	49.47	NaN	NaN
397	Bj Crombeen	2012-2013	T.B	R	44	487.65	1	7	8	6	...	2.00	100.42	23.61	18.65	29.780000	40.89	32.10	55.86	NaN	NaN
398	Bj Crombeen	2013-2014	T.B	R	55	559.17	3	7	10	5	...	5.08	97.07	39.56	17.15	28.180000	45.56	31.14	55.04	NaN	NaN
399	Bj Crombeen	2014-2015	ARI	R	58	469.48	3	3	6	4	...	6.98	98.99	43.62	13.85	26.730000	47.95	27.86	49.48	NaN	NaN
432	Boo Nieves	2017-2018	NYR	C	28	285.56	1	8	9	5	...	4.35	106.26	43.17	17.21	28.920000	43.84	28.91	51.83	NaN	NaN
445	Brad Hunt	2014-2015	EDM	D	11	214.32	1	2	3	3	...	5.00	95.11	63.49	31.43	32.180000	54.15	29.91	46.61	NaN	NaN
491	Brandon Gormley	2014-2015	ARI	D	27	413.72	2	2	4	3	...	5.13	95.02	48.99	25.54	27.500000	46.62	30.07	52.38	NaN	NaN
497	Brandon Mcmillan	2011-2012	ANA	C	25	280.39	0	4	4	1	...	0.00	96.26	32.56	18.74	30.920000	41.02	33.44	58.62	NaN	NaN
524	Brayden Mcnabb	2011-2012	BUF	D	25	445.96	1	7	8	3	...	4.35	100.90	46.52	29.43	27.960000	47.68	30.06	51.68	NaN	NaN
525	Brayden Mcnabb	2013-2014	BUF	D	12	206.72	0	0	0	0	...	0.00	103.23	44.76	28.01	29.180000	46.94	31.31	48.45	NaN	NaN
590	Brett Connolly	2011-2012	T.B	R	68	780.04	4	11	15	8	...	4.26	99.39	53.12	19.15	30.070000	50.81	29.30	46.54	NaN	NaN
601	Brett Ritchie	2014-2015	DAL	R	31	433.57	6	3	9	7	...	7.69	99.07	66.11	23.27	33.480000	57.12	30.47	44.36	NaN	NaN
624	Brian Gibbons	2014-2015	CBJ	C	25	345.60	0	5	5	3	...	0.00	102.02	51.52	23.01	31.030000	46.11	31.85	54.15	NaN	NaN
635	Brian Lee	2012-2013	T.B	D	22	306.26	0	0	0	0	...	0.00	92.88	43.15	23.49	27.080000	47.07	29.22	50.94	NaN	NaN
769	Charlie Mcavoy	2017-2018	BOS	D	63	1395.29	7	24	31	17	...	9.09	102.23	57.01	36.75	30.160000	54.17	31.08	48.57	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3946	Teemu Pulkkinen	2014-2015	DET	L	31	355.76	5	3	8	6	...	7.46	104.87	71.86	19.31	32.570000	59.27	29.92	42.82	NaN	NaN
3957	Tom Hickey	2012-2013	NYI	D	39	657.51	1	3	4	2	...	2.50	99.28	52.27	27.85	28.560000	48.68	30.22	51.87	NaN	NaN
3970	Tim Erixon	2011-2012	NYR	D	18	233.92	0	2	2	1	...	0.00	99.95	51.89	21.76	26.580000	49.31	28.42	48.26	NaN	NaN
3978	Tim Kennedy	2011-2012	FLA	L	27	300.59	1	1	2	2	...	4.55	91.02	52.94	18.74	28.380000	50.07	28.68	48.94	NaN	NaN
3979	Tim Kennedy	2013-2014	ARI	L	37	435.14	2	6	8	6	...	4.76	99.47	54.98	19.90	30.510000	50.42	29.29	48.76	NaN	NaN
3982	Tim Schaller	2014-2015	BUF	C	18	204.29	1	1	2	2	...	5.26	90.95	26.40	18.82	27.120000	35.63	29.83	53.47	NaN	NaN
3986	Tim Wallace	2012-2013	CAR	R	28	280.79	1	1	2	2	...	5.00	92.38	51.50	16.87	27.980000	48.81	30.75	52.85	NaN	NaN
4004	Tobias Enstrom	2011-2012	WPG	D	62	1476.47	6	26	32	17	...	6.52	99.30	58.13	39.15	33.140000	53.34	32.05	47.13	NaN	NaN
4023	Tomas Hertl	2013-2014	S.J	C	37	567.13	15	10	25	20	...	15.31	101.49	55.81	25.26	30.710000	58.45	30.28	45.79	NaN	NaN
4045	Tomas Tatar	2012-2013	DET	C	18	204.74	4	3	7	6	...	12.50	98.88	58.47	18.97	29.000000	56.57	28.56	43.43	NaN	NaN
4080	Tony Deangelo	2017-2018	NYR	D	32	530.53	0	8	8	6	...	0.00	94.98	68.23	27.75	28.520000	50.63	29.69	46.25	NaN	NaN
4111	Trent Hunter	2011-2012	L.A	R	38	383.73	2	5	7	5	...	3.92	97.57	50.24	16.93	29.730000	56.87	28.60	47.00	NaN	NaN
4125	Trevor Smith	2013-2014	TOR	C	28	290.43	4	4	8	7	...	16.67	99.04	39.42	17.70	29.670000	44.51	29.07	48.86	NaN	NaN
4146	Tye Mcginn	2012-2013	PHI	L	18	228.96	3	2	5	3	...	9.09	99.38	56.13	21.65	32.260000	52.30	30.59	45.77	NaN	NaN
4220	Victor Antipin	2017-2018	BUF	D	47	718.05	0	10	10	4	...	0.00	98.28	58.00	25.75	27.680000	49.17	29.19	48.35	NaN	NaN
4221	Victor Bartley	2012-2013	NSH	D	24	469.02	0	6	6	4	...	0.00	100.78	44.31	32.50	27.980000	46.48	29.96	50.58	NaN	NaN
4252	Vincent Trocheck	2013-2014	FLA	C	20	377.74	5	3	8	6	...	13.16	92.75	47.86	31.26	31.570000	51.67	32.76	51.03	NaN	NaN
4253	Vincent Trocheck	2014-2015	FLA	C	50	700.27	7	15	22	19	...	7.87	101.09	58.45	23.06	30.490000	53.18	29.14	47.59	NaN	NaN
4260	Vinny Prospal	2011-2012	CBJ	L	82	1466.71	16	38	54	41	...	9.70	99.10	62.71	29.63	34.010000	55.04	31.49	44.14	NaN	NaN
4261	Vinny Prospal	2012-2013	CBJ	L	48	793.28	12	18	30	17	...	14.12	102.10	62.76	27.15	32.650000	52.92	30.30	44.67	NaN	NaN
4283	Will Acton	2013-2014	EDM	R	30	244.13	3	2	5	4	...	16.67	98.56	27.71	13.70	28.000000	36.71	32.32	58.77	NaN	NaN
4297	Wojtek Wolski	2012-2013	WSH	L	27	360.45	4	5	9	8	...	8.16	98.92	47.71	22.35	31.580000	52.18	29.93	46.97	NaN	NaN
4298	Xavier Ouellet	2014-2015	DET	D	21	344.20	2	1	3	3	...	7.41	102.07	61.31	27.01	27.690000	52.26	28.88	49.55	NaN	NaN
4309	Yohann Auvitu	2016-2017	N.J	D	25	390.10	2	2	4	3	...	3.70	100.00	71.43	25.95	29.460000	52.06	29.35	45.42	NaN	NaN
4319	Zach Boychuk	2014-2015	CAR	L	31	329.41	3	3	6	5	...	8.57	100.36	53.00	18.22	28.550000	51.48	28.97	48.76	NaN	NaN
4320	Zach Hyman	2015-2016	TOR	C	16	250.97	4	2	6	6	...	10.81	98.09	62.35	25.94	31.080000	50.51	30.87	52.24	NaN	NaN
4332	Zach Sill	2013-2014	PIT	C	20	215.88	0	0	0	0	...	0.00	94.55	30.68	18.09	28.140000	42.48	32.00	57.14	NaN	NaN
4333	Zach Trotman	2014-2015	BOS	D	27	442.99	1	4	5	1	...	2.17	97.79	59.33	27.22	28.700000	52.29	29.81	50.14	NaN	NaN
4337	Zach Kassian	2012-2013	VAN	R	39	525.73	7	4	11	9	...	14.29	100.52	51.84	22.35	31.680000	56.59	29.24	45.69	NaN	NaN
4355	Zach Rinaldo	2015-2016	BOS	C	52	431.40	1	2	3	2	...	2.63	100.30	47.44	14.12	26.900000	46.43	27.77	50.00	NaN	NaN

293 rows × 46 columns

# Previously run when I was deciding to drop players that haven't played enough to 'qualify' for my model
# I ended up just rerunning the data from the website I pulled from to only account for players with above 200 minutes played
# df_all_cap[(df_all_cap['TOI'] > 150) & (df_all_cap['cap_hit'].isnull() == True)]

# Some players for some years I just simply don't have the data
df_cap[df_cap['Player'].str.contains('Hinostroza') == True]

	cap_hit	Player	team_name	Season
1381	717500	Vincent Hinostroza	chicago-blackhawks	2015-2016
1413	717500	Vincent Hinostroza	chicago-blackhawks	2016-2017
1449	717500	Vincent Hinostroza	chicago-blackhawks	2017-2018

# Confirming other missed records
df_all_cap[(df_all_cap['Player'].str.contains('Zach') == True) & (df_all_cap['cap_hit'].isnull() == True)]

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC	cap_hit	team_name
4319	Zach Boychuk	2014-2015	CAR	L	31	329.41	3	3	6	5	...	8.57	100.36	53.00	18.22	28.55	51.48	28.97	48.76	NaN	NaN
4320	Zach Hyman	2015-2016	TOR	C	16	250.97	4	2	6	6	...	10.81	98.09	62.35	25.94	31.08	50.51	30.87	52.24	NaN	NaN
4332	Zach Sill	2013-2014	PIT	C	20	215.88	0	0	0	0	...	0.00	94.55	30.68	18.09	28.14	42.48	32.00	57.14	NaN	NaN
4333	Zach Trotman	2014-2015	BOS	D	27	442.99	1	4	5	1	...	2.17	97.79	59.33	27.22	28.70	52.29	29.81	50.14	NaN	NaN
4337	Zach Kassian	2012-2013	VAN	R	39	525.73	7	4	11	9	...	14.29	100.52	51.84	22.35	31.68	56.59	29.24	45.69	NaN	NaN
4355	Zach Rinaldo	2015-2016	BOS	C	52	431.40	1	2	3	2	...	2.63	100.30	47.44	14.12	26.90	46.43	27.77	50.00	NaN	NaN

6 rows × 46 columns

I’ve spent a great deal of time trying to match up the player performance/cap data on player name and season.
This in cludes really digging around in the data to determine why the two sets aren’t matching.
I was able to reduce the non-matchups from about 950 to 313.
I’m going to drop the rest of those without cap data as most of the remaining players I simply cannot find the data for anywhere online.
Most of these players didn’t play significant time for their respective team, so it shouldn’t be as big of a deal.

# I'd say 4013 data points is still sufficient to do my analysis
df_all_cap.dropna(axis=0, how='any').shape
# df_es_cap.dropna(axis=0, how='any').shape

(4083, 46)

# Actual drop
df_all_cap.dropna(axis=0, how='any', inplace=True)
# df_es_cap.dropna(axis=0, how='any', inplace=True)

# Check
df_all_cap[df_all_cap['cap_hit'].isnull() == True]

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC	cap_hit	team_name

0 rows × 46 columns

df_all_cap[df_all_cap['Team'] == 'VGK']

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC	cap_hit	team_name
172	Alex Tuch	2017-2018	VGK	R	78	1188.51	15	21	36	30	...	8.77	100.98	63.89	25.36	32.13	56.25	30.43	45.24	925000.0	vegas-golden-knights
446	Brad Hunt	2017-2018	VGK	D	45	748.69	3	14	17	10	...	3.85	101.07	64.20	27.86	29.63	56.98	29.08	45.29	650000.0	vegas-golden-knights
529	Brayden Mcnabb	2017-2018	VGK	D	76	1530.10	5	10	15	7	...	5.68	101.14	40.59	33.74	29.74	47.32	32.95	55.19	1700000.0	vegas-golden-knights
896	Cody Eakin	2017-2018	VGK	C	80	1162.48	11	16	27	18	...	10.38	97.84	46.07	24.21	30.64	49.58	31.21	53.04	3850000.0	vegas-golden-knights
932	Colin Miller	2017-2018	VGK	D	82	1585.16	10	30	40	28	...	5.65	98.98	66.42	32.00	29.85	52.29	30.27	47.38	1000000.0	vegas-golden-knights
1161	David Perron	2017-2018	VGK	L	70	1245.71	15	50	65	49	...	12.10	102.37	61.00	29.38	32.69	55.80	31.34	46.08	3750000.0	vegas-golden-knights
1247	Deryk Engelland	2017-2018	VGK	D	79	1600.25	5	18	23	18	...	4.63	100.24	38.63	33.85	29.27	49.03	32.32	55.24	1000000.0	vegas-golden-knights
1427	Erik Haula	2017-2018	VGK	L	76	1319.06	29	26	55	40	...	16.57	99.57	60.53	28.68	33.21	54.47	31.33	46.62	2750000.0	vegas-golden-knights
1694	James Neal	2017-2018	VGK	L	71	1217.25	25	18	43	36	...	12.44	99.93	62.62	28.31	33.15	55.55	31.09	45.41	5000000.0	vegas-golden-knights
2060	Jonathan Marchessault	2017-2018	VGK	C	77	1347.85	27	47	74	60	...	10.11	104.52	61.75	28.92	33.32	54.42	31.41	45.73	750000.0	vegas-golden-knights
2076	Jon Merrill	2017-2018	VGK	D	34	546.90	1	2	3	2	...	3.33	102.05	51.72	27.09	26.93	50.78	29.11	50.55	1137500.0	vegas-golden-knights
2450	Luca Sbisa	2017-2018	VGK	D	30	585.39	2	10	12	6	...	7.69	103.54	36.03	32.65	29.56	48.94	32.63	54.26	3600000.0	vegas-golden-knights
3048	Nate Schmidt	2017-2018	VGK	D	76	1690.23	5	29	34	19	...	4.85	102.27	48.50	36.63	29.93	51.87	32.54	51.07	2225000.0	vegas-golden-knights
3254	Oscar Lindberg	2017-2018	VGK	C	63	741.82	9	2	11	10	...	11.25	95.89	49.67	19.88	29.39	49.53	29.39	52.04	1700000.0	vegas-golden-knights
3415	Pierre-Edouard Bellemare	2017-2018	VGK	L	72	886.86	6	10	16	12	...	6.52	99.19	34.38	20.72	29.62	45.25	33.10	56.21	1450000.0	vegas-golden-knights
3471	Reilly Smith	2017-2018	VGK	R	67	1200.92	22	39	61	45	...	13.58	104.10	51.78	29.59	34.64	52.94	33.11	49.80	5000000.0	vegas-golden-knights
3818	Shea Theodore	2017-2018	VGK	D	61	1241.49	6	23	29	20	...	4.17	100.23	61.70	33.66	30.45	54.64	30.57	46.62	863333.0	vegas-golden-knights
4038	Tomas Nosek	2017-2018	VGK	L	67	743.13	7	8	15	13	...	7.61	100.71	43.09	18.74	29.00	48.38	31.47	54.18	612500.0	vegas-golden-knights
4286	William Carrier	2017-2018	VGK	L	37	327.11	1	2	3	2	...	1.92	97.86	56.31	14.96	26.97	50.92	28.25	49.59	690000.0	vegas-golden-knights
4289	William Karlsson	2017-2018	VGK	C	82	1535.05	42	35	77	56	...	22.95	104.34	55.32	30.86	33.94	51.70	33.01	49.46	1000000.0	vegas-golden-knights

20 rows × 46 columns

# Save my work
df_all_cap.to_csv('./data/all_cap_semicleaned.csv')

df_all_cap.head()

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC	cap_hit	team_name
0	Aaron Ekblad	2014-2015	FLA	D	81	1766.60	12	27	39	22	...	7.06	100.62	66.19	35.68	30.40	53.99	30.17	46.05	1775000.0	florida-panthers
1	Aaron Ekblad	2015-2016	FLA	D	78	1690.83	15	20	35	23	...	8.24	101.92	60.39	35.80	31.18	52.08	30.95	47.13	925000.0	florida-panthers
2	Aaron Ekblad	2016-2017	FLA	D	68	1459.29	10	11	21	14	...	4.44	97.21	64.51	35.12	30.87	54.64	30.87	47.02	925000.0	florida-panthers
3	Aaron Ekblad	2017-2018	FLA	D	82	1917.89	16	22	38	22	...	8.47	101.72	41.08	38.65	33.37	52.47	33.19	51.10	7500000.0	florida-panthers
4	Aaron Johnson	2011-2012	CBJ	D	56	924.40	3	13	16	9	...	4.76	98.13	41.93	27.55	27.78	47.36	30.73	51.72	550000.0	columbus-blue-jackets

5 rows × 46 columns

Part II - Adding in Team stats

Note: After much careful deliberation and evaluation, I’ve decided to scrap using team data altogether. I’ll only be evaluating player performance vs salary (% of team salary will stay though). I’ll be leaving this section here though to show my work. To be quite honest, I think I ran into an issue of project scope creep. I originally intended to see how players help their team win, and see if that factors into value, but that would be too far off track of my original “How much is a player worth based on his production? What players are undervalued and can be acquired for less?”

Original Idea here: I’m adding in overall team stats to get a sense of what may be contributing to a team’s performance from an individual player’s perspective.

# Team data - So I can start fresh if need
df_team = pd.read_csv('./data/team_stats.csv')

df_team.head()

	Team	Season	GP	TOI	CF	CA	C+/-	CF%	CF/60	CA/60	...	xG+/-	xGF%	xGF/60	xGA/60	PENT	PEND	P+/-	Sh%	Sv%	PDO
0	ANA	2011-2012	82	3890.68	3326	3526	-200	48.54	51.29	54.38	...	-16.20	47.13	2.05	2.30	274	260	-14	7.99	91.66	99.65
1	ANA	2012-2013	48	2319.80	1970	2140	-170	47.93	50.95	55.35	...	0.22	50.07	2.12	2.11	162	137	-25	8.59	93.01	101.60
2	ANA	2013-2014	82	3822.90	3500	3528	-28	49.80	54.93	55.37	...	8.64	51.45	2.40	2.27	292	293	1	9.83	92.62	102.45
3	ANA	2014-2015	82	3902.03	3610	3474	136	50.96	55.51	53.42	...	12.21	52.13	2.29	2.10	263	251	-12	8.48	91.82	100.30
4	ANA	2015-2016	82	3882.25	3646	3310	336	52.42	56.35	51.16	...	16.58	53.01	2.26	2.00	329	272	-57	6.69	92.39	99.08

5 rows × 28 columns

df_team['Team'].unique()

array(['ANA', 'ARI', 'BOS', 'BUF', 'CAR', 'CBJ', 'CGY', 'CHI', 'COL',
       'DAL', 'DET', 'EDM', 'FLA', 'L.A', 'MIN', 'MTL', 'N.J', 'NSH',
       'NYI', 'NYR', 'OTT', 'PHI', 'PIT', 'S.J', 'STL', 'T.B', 'TOR',
       'VAN', 'VGK', 'WPG', 'WSH'], dtype=object)

# Vegas is the additional team present in team stats, but not in player performance + cap
df_all_cap['Team'].sort_values().unique()

array(['ANA', 'ARI', 'BOS', 'BUF', 'CAR', 'CBJ', 'CGY', 'CHI', 'COL',
       'DAL', 'DET', 'EDM', 'FLA', 'L.A', 'MIN', 'MTL', 'N.J', 'NSH',
       'NYI', 'NYR', 'OTT', 'PHI', 'PIT', 'S.J', 'STL', 'T.B', 'TOR',
       'VAN', 'VGK', 'WPG', 'WSH'], dtype=object)

As the Vegas Golden Knights have only had one season, I’m going to remove them from this data. It’s tough to get a read on that team with only one year worth of data (and you can’t judge Y2Y performance that way either).
The only full season they played, you’d have an entirely new coach, working with an entirely new team, in an entirely new system. None of the other teams would be able to scout that team as well b/c there’s simply no tape on them (and scounting and preparing a gameplan for teams is essential to winning).

df_team[df_team['Team'] == 'VGK']

	Team	Season	GP	TOI	CF	CA	C+/-	CF%	CF/60	CA/60	...	xG+/-	xGF%	xGF/60	xGA/60	PENT	PEND	P+/-	Sh%	Sv%	PDO
196	VGK	2017-2018	82	3920.4	3819	3675	144	50.96	58.45	56.24	...	4.05	50.68	2.33	2.26	209	233	24	8.38	92.19	100.57

1 rows × 28 columns

df_team = df_team.drop(index=196, axis=0).reset_index(drop=True)

# Check
df_team[194:198]

	Team	Season	GP	TOI	CF	CA	C+/-	CF%	CF/60	CA/60	...	xG+/-	xGF%	xGF/60	xGA/60	PENT	PEND	P+/-	Sh%	Sv%	PDO
194	VAN	2016-2017	82	4046.86	3401	3691	-290	47.96	50.42	54.72	...	-25.40	45.71	2.01	2.38	221	227	6	7.01	92.42	99.43
195	VAN	2017-2018	81	3920.53	3451	3787	-336	47.68	52.81	57.96	...	-20.22	46.57	2.10	2.41	230	208	-22	7.21	92.15	99.36
196	WPG	2011-2012	82	3897.03	3643	3596	47	50.32	56.09	55.37	...	5.28	50.86	2.40	2.32	285	271	-14	7.68	91.74	99.43
197	WPG	2012-2013	48	2354.72	2195	2241	-46	49.48	55.93	57.10	...	-5.01	48.62	2.26	2.38	157	149	-8	8.30	91.20	99.50

4 rows × 28 columns

# Appending _team on the end so it does it for the whole df, and not just the columns that overlap
df_team.columns = [str(col) + '_team' for col in df_team.columns]

Merge in with Main Data Set

This section is now moot, as I’ve decided to drop using team data, but leaving here to show the work.

df_all_cap_team = pd.merge(
    df_all_cap,
    df_team,
    how='left',
    left_on=['Team','Season'],
    right_on=['Team_team','Season_team'])

# Save my work so far
df_all_cap_team.to_csv('./data/raw_combined.csv')

df_all_cap_team.head()

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	xG+/-_team	xGF%_team	xGF/60_team	xGA/60_team	PENT_team	PEND_team	P+/-_team	Sh%_team	Sv%_team	PDO_team
0	Aaron Ekblad	2014-2015	FLA	D	81	1766.60	12	27	39	22	...	9.08	51.71	2.10	1.96	254.0	263.0	9.0	7.27	92.38	99.65
1	Aaron Ekblad	2015-2016	FLA	D	78	1690.83	15	20	35	23	...	-5.14	49.02	2.00	2.08	280.0	291.0	11.0	8.88	93.28	102.17
2	Aaron Ekblad	2016-2017	FLA	D	68	1459.29	10	11	21	14	...	-12.11	47.86	2.08	2.27	277.0	277.0	0.0	6.56	92.10	98.66
3	Aaron Ekblad	2017-2018	FLA	D	82	1917.89	16	22	38	22	...	-8.06	48.72	2.34	2.46	262.0	295.0	33.0	7.71	92.32	100.03
4	Aaron Johnson	2011-2012	CBJ	D	56	924.40	3	13	16	9	...	-5.15	49.01	2.00	2.09	300.0	325.0	25.0	7.34	91.78	99.12

5 rows × 74 columns

df_all_cap_team.columns

Index(['Player', 'Season', 'Team', 'Position', 'GP', 'TOI', 'G', 'A', 'P',
       'P1', 'P/60', 'P1/60', 'GS', 'GS/60', 'CF', 'CA', 'C+/-', 'CF%',
       'Rel CF%', 'GF', 'GA', 'G+/-', 'GF%', 'Rel GF%', 'xGF', 'xGA', 'xG+/-',
       'xGF%', 'Rel xGF%', 'iPENT', 'iPEND', 'iP+/-', 'iCF', 'iCF/60', 'ixGF',
       'ixGF/60', 'iSh%', 'PDO', 'ZSR', 'TOI%', 'TOI% QoT', 'CF% QoT',
       'TOI% QoC', 'CF% QoC', 'cap_hit', 'team_name', 'Team_team',
       'Season_team', 'GP_team', 'TOI_team', 'CF_team', 'CA_team', 'C+/-_team',
       'CF%_team', 'CF/60_team', 'CA/60_team', 'GF_team', 'GA_team',
       'G+/-_team', 'GF%_team', 'GF/60_team', 'GA/60_team', 'xGF_team',
       'xGA_team', 'xG+/-_team', 'xGF%_team', 'xGF/60_team', 'xGA/60_team',
       'PENT_team', 'PEND_team', 'P+/-_team', 'Sh%_team', 'Sv%_team',
       'PDO_team'],
      dtype='object')

df_all_cap_team.dtypes

Player          object
Season          object
Team            object
Position        object
GP               int64
TOI            float64
G                int64
A                int64
P                int64
P1               int64
P/60           float64
P1/60          float64
GS             float64
GS/60          float64
CF               int64
CA               int64
C+/-             int64
CF%            float64
Rel CF%        float64
GF               int64
GA               int64
G+/-             int64
GF%            float64
Rel GF%        float64
xGF            float64
xGA            float64
xG+/-          float64
xGF%           float64
Rel xGF%       float64
iPENT            int64
                ...   
cap_hit        float64
team_name       object
Team_team       object
Season_team     object
GP_team        float64
TOI_team       float64
CF_team        float64
CA_team        float64
C+/-_team      float64
CF%_team       float64
CF/60_team     float64
CA/60_team     float64
GF_team        float64
GA_team        float64
G+/-_team      float64
GF%_team       float64
GF/60_team     float64
GA/60_team     float64
xGF_team       float64
xGA_team       float64
xG+/-_team     float64
xGF%_team      float64
xGF/60_team    float64
xGA/60_team    float64
PENT_team      float64
PEND_team      float64
P+/-_team      float64
Sh%_team       float64
Sv%_team       float64
PDO_team       float64
Length: 74, dtype: object

# No nulls. Nice!
df_all_cap_team.isnull().sum().sort_values()

Player          0
xGA             0
xG+/-           0
xGF%            0
Rel xGF%        0
iPENT           0
iPEND           0
iP+/-           0
iCF             0
iCF/60          0
xGF             0
ixGF            0
PDO             0
ZSR             0
TOI%            0
TOI% QoT        0
CF% QoT         0
TOI% QoC        0
CF% QoC         0
cap_hit         0
team_name       0
ixGF/60         0
Rel GF%         0
iSh%            0
G+/-            0
GF%             0
Season          0
Team            0
Position        0
GP              0
               ..
P1/60           0
GA              0
Sh%_team       20
GA/60_team     20
xGF_team       20
xGA_team       20
xG+/-_team     20
xGF%_team      20
PEND_team      20
xGA/60_team    20
PENT_team      20
P+/-_team      20
GF/60_team     20
xGF/60_team    20
GF%_team       20
GP_team        20
GA_team        20
GF_team        20
CA/60_team     20
CF/60_team     20
CF%_team       20
C+/-_team      20
CA_team        20
CF_team        20
TOI_team       20
Season_team    20
Team_team      20
Sv%_team       20
G+/-_team      20
PDO_team       20
Length: 74, dtype: int64

# Now that team data is attached to each player that played for the same team all year
# I can drop redudant columns:
df_all_cap_team.drop(axis=1, columns=['team_name','Team_team','Season_team'], inplace=True)

Explaining Important Advanced Metrics

Important Assumption

I think it’s important to highlight a key assumption:

It is assumed that the team that controls the puck more, generates more score chances, and therefore scores more goals, and is more likely to win the game. This is an important underlying assumption to many NHL advanced stats, and hence why they are tracked closely. Though it is not conclusively proven that better possessions = wins, I will be accepting this assumption for now.

Key Advanced Metrics

A key metric is Corsi. Corsi serves as a proxy measure for puck possession during five-on-five play. To calculate a Corsi number, you add shots on against the opposing team’s net, missed shots for and blocked shots against. Next, you subtract shots on target against, missed shots against, and blocked shots for. Corsi is important to understand which team or player is generating, or attempting to generate the most scoring opportunities. Essentially, it is a percentage of the amount of shots a player generates towards their opponent’s net, minus the shots attempts that are thrown at that player’s team’s net.

Fenwick is another popular stat. It is essentially the same as Corsi, but it doesn’t take into account blocked shots. It has been shown to be a better predictor of possession. Despite this, it isn’t used as often as Corsi.

Both Corsi and Fenwick can be applied to an individual or an entire team. And both can also be applied on an offensive only (CF or Corsi For) or defensive only (CA or Corsi Against) basis.

The numbers for Corsi and Fenwick generally fall between 40 and 60%. A team (or player) with a Corsi/Fenwick of 55% and above would be considered ‘elite,’ whereas lower than 45% is generally considered subpar.

PDO is another popular advanced metric thrown around alot. The acroynm doesn’t stand for anything actually, and is simply 5v5 shooting percentage + 5v5 save percentage. Its importance is that it is a commonly understood as a strong indicator of luck. The average PDO for the league is around 100, so a team under that number could be said to be ‘unlucky,’ and a team above it could be said to be ‘lucky.’ This is important as stats gurus interpret this is a reflection of if a team is outperforming, or underperforming, due to luck, and is due to revert back to the mean 100.

Most of the other metrics are based off of these metrics. Some extra background first: in standard play, only 5 skaters (3 forwards & 2 defensemen - note ‘skaters’ excludes goaltenders) from each side are allowed on the ice at a given time. However, on a normal night, each team has 18 skaters: 12 forwards and 6 defensemen. This means that there are 4 forward line combinations, and 3 defensemen pairings. In a 60 minutes game (3 periods - 20 minutes a piece), not everyone is going to get equal ice time. As a baseline example, the best forwards in the league get around 18 - 22 minutes of ice time a night, and the best defensemen will get 25 - 28 minutes (some upwards of 30). The coaches are ultimately the ones who decide how much ice time each player gets. To normalize player’s ice time, and get a better sense of whose making the most of their time, Corsi/Fenwick and other advanced stats are averaged out as if each player played a full 60 minutes (so Corsi For per 60, Corsi Against per 60, etc). The formula is as such: 5v5 ‘Y’/60 = 5v5 Base Statistic Y/5v5 Time On Ice * 60

Each of these stats can also be applied to a player, relative to their overall team when that player is not on the ice. So say a player with a Relative Corsi For Percentage of 5.55% means that that player generates 5.55% more shot attempts at his opponents net than the average player on his team. Otherwise, great players on bad teams may objectively appear worse than average players on a great team (where the quality of a team is measured based on how good they are at driving possession). That formula looks like this: Corsi For % Relative (CF% Rel) = CF% Player A – CF% of team when Player A off-ice.

EDA

First off, going to read in my data anew. Essentially as a checkpoint. This set should equal my df_all_cap from way above.

df = pd.read_csv('./data/all_cap_semicleaned.csv')

df.drop(axis=1, columns='Unnamed: 0', inplace=True)

df.head()

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	iSh%	PDO	ZSR	TOI%	TOI% QoT	CF% QoT	TOI% QoC	CF% QoC	cap_hit	team_name
0	Aaron Ekblad	2014-2015	FLA	D	81	1766.60	12	27	39	22	...	7.06	100.62	66.19	35.68	30.40	53.99	30.17	46.05	1775000.0	florida-panthers
1	Aaron Ekblad	2015-2016	FLA	D	78	1690.83	15	20	35	23	...	8.24	101.92	60.39	35.80	31.18	52.08	30.95	47.13	925000.0	florida-panthers
2	Aaron Ekblad	2016-2017	FLA	D	68	1459.29	10	11	21	14	...	4.44	97.21	64.51	35.12	30.87	54.64	30.87	47.02	925000.0	florida-panthers
3	Aaron Ekblad	2017-2018	FLA	D	82	1917.89	16	22	38	22	...	8.47	101.72	41.08	38.65	33.37	52.47	33.19	51.10	7500000.0	florida-panthers
4	Aaron Johnson	2011-2012	CBJ	D	56	924.40	3	13	16	9	...	4.76	98.13	41.93	27.55	27.78	47.36	30.73	51.72	550000.0	columbus-blue-jackets

5 rows × 46 columns

# Last column is redundant, dropping...
df.drop(axis=1, columns='team_name', inplace=True)

df.shape

(4083, 45)

df.describe().T

	count	mean	std	min	25%	50%	75%	max
GP	4083.0	6.111144e+01	1.939716e+01	11.00	47.000	67.00	79.000	8.200000e+01
TOI	4083.0	1.037358e+03	4.569568e+02	200.32	674.710	1043.88	1385.495	2.411970e+03
G	4083.0	9.707568e+00	8.790652e+00	0.00	3.000	7.00	14.000	6.000000e+01
A	4083.0	1.602082e+01	1.223425e+01	0.00	6.000	13.00	23.000	7.000000e+01
P	4083.0	2.572839e+01	1.943214e+01	0.00	10.000	20.00	37.000	1.090000e+02
P1	4083.0	1.871173e+01	1.513694e+01	0.00	7.000	14.00	27.000	9.600000e+01
P/60	4083.0	1.403674e+00	7.484710e-01	0.00	0.810	1.29	1.920	5.320000e+00
P1/60	4083.0	1.028555e+00	6.214068e-01	0.00	0.520	0.94	1.450	3.720000e+00
GS	4083.0	2.801139e+01	2.182418e+01	-12.53	10.600	22.90	41.790	1.239400e+02
GS/60	4083.0	1.507389e+00	8.805259e-01	-1.34	0.870	1.43	2.090	5.480000e+00
CF	4083.0	9.793691e+02	4.965451e+02	121.00	570.000	949.00	1359.000	2.588000e+03
CA	4083.0	9.432454e+02	4.277688e+02	138.00	608.000	952.00	1235.500	2.295000e+03
C+/-	4083.0	3.612368e+01	2.634875e+02	-882.00	-126.000	10.00	196.500	1.011000e+03
CF%	4083.0	5.014169e+01	6.490354e+00	28.13	45.510	50.36	55.105	6.849000e+01
Rel CF%	4083.0	5.159172e-01	7.935015e+00	-24.38	-5.555	0.77	6.695	2.272000e+01
GF	4083.0	4.765883e+01	2.922184e+01	0.00	23.000	43.00	69.000	1.420000e+02
GA	4083.0	4.525300e+01	2.301080e+01	3.00	27.000	45.00	61.000	1.220000e+02
G+/-	4083.0	2.405829e+00	1.886229e+01	-51.00	-9.000	0.00	13.000	7.600000e+01
GF%	4083.0	4.946605e+01	1.040993e+01	0.00	42.860	50.00	56.885	8.000000e+01
Rel GF%	4083.0	-3.652878e-01	1.265023e+01	-56.05	-9.370	-0.03	9.055	3.677000e+01
xGF	4083.0	4.712235e+01	2.683599e+01	3.95	24.785	43.41	66.555	1.305600e+02
xGA	4083.0	4.494826e+01	2.226387e+01	5.76	27.030	44.66	60.165	1.146400e+02
xG+/-	4083.0	2.174090e+00	1.512175e+01	-46.21	-7.160	0.31	10.540	6.128000e+01
xGF%	4083.0	5.003109e+01	7.737625e+00	23.96	44.680	50.20	55.785	7.150000e+01
Rel xGF%	4083.0	2.728190e-01	9.747457e+00	-28.12	-7.080	0.53	7.825	2.846000e+01
iPENT	4083.0	1.340069e+01	8.557877e+00	0.00	7.000	12.00	18.000	6.200000e+01
iPEND	4083.0	1.197355e+01	8.024875e+00	0.00	6.000	11.00	16.000	6.000000e+01
iP+/-	4083.0	-1.427137e+00	7.392361e+00	-30.00	-6.000	-1.00	3.000	4.000000e+01
iCF	4083.0	1.975400e+02	1.164728e+02	14.00	106.000	179.00	270.500	8.240000e+02
iCF/60	4083.0	1.118595e+01	3.623528e+00	2.99	8.540	10.79	13.500	3.003000e+01
ixGF	4083.0	9.540103e+00	7.484102e+00	0.24	3.490	7.57	13.900	3.982000e+01
ixGF/60	4083.0	5.453490e-01	3.227925e-01	0.04	0.240	0.55	0.780	1.950000e+00
iSh%	4083.0	8.233987e+00	4.728038e+00	0.00	4.650	8.05	11.455	3.000000e+01
PDO	4083.0	9.983670e+01	2.439248e+00	88.06	98.320	99.91	101.440	1.100900e+02
ZSR	4083.0	5.070726e+01	1.137525e+01	8.57	42.860	51.28	59.080	8.840000e+01
TOI%	4083.0	2.751694e+01	6.456828e+00	8.11	22.860	27.66	31.765	4.779000e+01
TOI% QoT	4083.0	inf	NaN	25.17	29.120	30.94	33.440	inf
CF% QoT	4083.0	5.063687e+01	4.184097e+00	32.81	47.660	50.72	53.800	6.435000e+01
TOI% QoC	4083.0	inf	NaN	26.40	30.050	31.26	32.370	inf
CF% QoC	4083.0	4.999643e+01	3.627037e+00	40.44	47.045	49.78	52.860	6.100000e+01
cap_hit	4083.0	2.601426e+06	2.084204e+06	77500.00	863333.000	1800000.00	4000000.000	1.050000e+07

df.dtypes

Player       object
Season       object
Team         object
Position     object
GP            int64
TOI         float64
G             int64
A             int64
P             int64
P1            int64
P/60        float64
P1/60       float64
GS          float64
GS/60       float64
CF            int64
CA            int64
C+/-          int64
CF%         float64
Rel CF%     float64
GF            int64
GA            int64
G+/-          int64
GF%         float64
Rel GF%     float64
xGF         float64
xGA         float64
xG+/-       float64
xGF%        float64
Rel xGF%    float64
iPENT         int64
iPEND         int64
iP+/-         int64
iCF           int64
iCF/60      float64
ixGF        float64
ixGF/60     float64
iSh%        float64
PDO         float64
ZSR         float64
TOI%        float64
TOI% QoT    float64
CF% QoT     float64
TOI% QoC    float64
CF% QoC     float64
cap_hit     float64
dtype: object

I’m going to convert cap_hit to an integer, as player’s yearly cap hit never includes fractions of a dollar.

df['cap_hit'] = df['cap_hit'].astype(int)

Upon further review, the minimum of the player’s cap hit is off. The first year I took data from was 2011-2012, when the minimum an NHL player could make was $525,000 / year (http://www.nhl.com/ice/page.htm?id=26366). How many cap_hits are under this minimum?

df[df['cap_hit'] < 525000].shape[0]
# Only 79 rows, going to consider dropping these.

df[df['cap_hit'] < 525000].index

Int64Index([], dtype='int64')

Note: Player contracts can pay different rates at the NHL level versus a minor league. By this I mean, the majority of players have one-way contracts, that is, they’re paid the same regardless if they play in the NHL or AHL (American Hockey League - the highest minor league behind the NHL - the NHL’s farm league essentially). However, some players, ones that may just be joining the league, or simply are ‘fringe’ NHL players, may have two-way contracts, that pay them radically different sums of money depending on if they play the NHL or AHL. It can be a drastic difference - say for example - having a two-way contract structure such that the player will make 75,000/year at the AHL level, but 525,000/year at the NHL level, isn’t unheard of in the NHL. This article does a pretty good job of explaining it: https://www.nhl.com/lightning/news/whats-the-difference-between-a-one-way-and-a-two-way-contract/c-726016.

This is an important issue to highlight as due to injuries, trades, or simply GMs trying to give their coaching staff the best/different players to work with, it is often the case that players are moved between leagues to fill roster spots and get different looks.

The issue here is the website I scrapped this data off of contains player salaries that may reflect a pro-rated pay. By this I mean, once a season is over, for two-way contract players that spent time in both the AHL and NHL, it’s common to calculate his yearly cap hit by weighting his salary to reflect time spent in the AHL (where he would be making less) against time spent in the NHL (where he would be making more) on a per-game basis.

As a result, I’m going to remove players that made less than league minimum, because that means they didn’t spend the entire year with their NHL club.

df = df.drop(axis=0, index=df[df['cap_hit'] < 525000].index).reset_index(drop=True)

df.isnull().sum()

Player      0
Season      0
Team        0
Position    0
GP          0
TOI         0
G           0
A           0
P           0
P1          0
P/60        0
P1/60       0
GS          0
GS/60       0
CF          0
CA          0
C+/-        0
CF%         0
Rel CF%     0
GF          0
GA          0
G+/-        0
GF%         0
Rel GF%     0
xGF         0
xGA         0
xG+/-       0
xGF%        0
Rel xGF%    0
iPENT       0
iPEND       0
iP+/-       0
iCF         0
iCF/60      0
ixGF        0
ixGF/60     0
iSh%        0
PDO         0
ZSR         0
TOI%        0
TOI% QoT    0
CF% QoT     0
TOI% QoC    0
CF% QoC     0
cap_hit     0
dtype: int64

# I know there's NAN values in my df, just having a tough time pulling them out
df.isnull().values.any()

False

# pd.set_option('display.max_row', 4000)
# df
# I manually dug around, and only TOI% QoT & TOI% QoC have this issue.
# I'm just going to drop them b/c I already have several TOI categories as is

df = df.drop(axis=1, columns=['TOI% QoT','TOI% QoC'])

df.columns

Index(['Player', 'Season', 'Team', 'Position', 'GP', 'TOI', 'G', 'A', 'P',
       'P1', 'P/60', 'P1/60', 'GS', 'GS/60', 'CF', 'CA', 'C+/-', 'CF%',
       'Rel CF%', 'GF', 'GA', 'G+/-', 'GF%', 'Rel GF%', 'xGF', 'xGA', 'xG+/-',
       'xGF%', 'Rel xGF%', 'iPENT', 'iPEND', 'iP+/-', 'iCF', 'iCF/60', 'ixGF',
       'ixGF/60', 'iSh%', 'PDO', 'ZSR', 'TOI%', 'CF% QoT', 'CF% QoC',
       'cap_hit'],
      dtype='object')

Let’s take a quick look at how player cap hit is distributed, since that’s going to be my dependent feature (y) here.

round(df['cap_hit'].mean(), 2)

2646777.01

# Take a look at general descriptive stats on cap_hit
print(f'''
Measurements of:
General Shape
    Skew = {stats.skew(df['cap_hit'])}
    Kurtosis = {stats.kurtosis(df['cap_hit'])}
Centeredness
    Mean = {round(df['cap_hit'].mean(), 0)}
    Median = {df['cap_hit'].median()}
Spread
    Standard Deviation = {round(df['cap_hit'].std(),0)}
    Range = {df['cap_hit'].max() - df['cap_hit'].min()}
    IQR = {stats.iqr(df['cap_hit'])}
''')

Measurements of:
General Shape
    Skew = 0.9308448301775509
    Kurtosis = 0.047143102770186296
Centeredness
    Mean = 2646777.0
    Median = 1900000.0
Spread
    Standard Deviation = 2079766.0
    Range = 9975000
    IQR = 3209208.0

# Doing a quick calc of how many bins I should use (max - min / a sensible bin breakdown for salaries)
(11000000 - 500000) / 250000

42.0

set_bins = np.arange(500000, 11000000, 250000)

# Make sure this works as intended
set_bins

array([  500000,   750000,  1000000,  1250000,  1500000,  1750000,
        2000000,  2250000,  2500000,  2750000,  3000000,  3250000,
        3500000,  3750000,  4000000,  4250000,  4500000,  4750000,
        5000000,  5250000,  5500000,  5750000,  6000000,  6250000,
        6500000,  6750000,  7000000,  7250000,  7500000,  7750000,
        8000000,  8250000,  8500000,  8750000,  9000000,  9250000,
        9500000,  9750000, 10000000, 10250000, 10500000, 10750000])

fig, ax = plt.subplots(figsize=(10, 10))
ax = sns.distplot(df['cap_hit'], bins = set_bins, kde=False, axlabel="Cap Hit (in thousands)", hist_kws=dict(edgecolor="k", linewidth=1), ax=ax)
# plt.title('Distribution of Player Cap Hits', fontsize=25)
plt.xticks(ticks=range(500000, 11000000, 250000), rotation=45, fontsize=9, ha='right')
plt.ticklabel_format(style='sci', axis='x', scilimits=(3,3))
plt.yticks(range(0, 850, 50), fontsize=20)
ax.set_xlabel("Cap Hit (in thousands)",fontsize=15)
ax.set_ylabel("Number of Players at Salary Level",fontsize=15)
plt.show()

/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6499: MatplotlibDeprecationWarning:
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  alternative="'density'", removal="3.1")

png

# Take a look at the variables most correlated with cap_hit
df.corr().loc[:, 'cap_hit'].sort_values(ascending=False)

cap_hit     1.000000
xGF         0.588115
GF          0.580287
CF          0.563647
A           0.559420
TOI%        0.547929
P           0.544977
GS          0.533783
iCF         0.528496
P1          0.506704
TOI         0.499160
xGA         0.454603
GA          0.451025
ixGF        0.432774
G           0.425146
CA          0.423818
P/60        0.387263
GS/60       0.372871
xG+/-       0.371745
C+/-        0.370356
Rel CF%     0.360337
Rel xGF%    0.360023
G+/-        0.346696
CF%         0.342212
P1/60       0.338613
xGF%        0.335001
Rel GF%     0.328721
CF% QoT     0.307650
GF%         0.301047
GP          0.280857
iCF/60      0.271576
ZSR         0.243439
ixGF/60     0.217491
iPENT       0.212628
iPEND       0.205219
PDO         0.159688
iSh%        0.148410
iP+/-      -0.023172
CF% QoC    -0.264072
Name: cap_hit, dtype: float64

Create Models

# Notes: Apply Standard Scaler at some point
# Try GridSearchCV
# Apply Pipeline

Model 1: Highest-correlation in features (r-score > .55)

For my first model, I’m going to simply create a few models (SLR and RF) where the features (independent variables) have an r-score above .55.

# pd.set_option('display.max_row', 100)
df[['xGF', 'GF', 'CF', 'A', 'P', 'TOI%']]

	xGF	GF	CF	A	P	TOI%
0	80.80	87	1760	27	39	35.68
1	81.95	93	1520	20	35	35.80
2	66.53	54	1549	11	21	35.12
3	101.42	115	1935	22	38	38.65
4	37.94	36	746	13	16	27.55
5	9.72	8	303	4	5	13.02
6	25.13	21	583	5	9	25.04
7	13.28	14	312	5	5	25.68
8	12.91	14	291	1	1	22.09
9	4.53	5	152	0	1	15.15
10	7.01	4	194	0	2	12.58
11	26.60	32	700	12	18	21.32
12	13.87	5	356	2	3	17.55
13	22.87	23	505	9	11	26.72
14	26.81	29	697	6	16	17.84
15	11.01	5	286	0	3	20.21
16	7.48	9	202	1	4	17.17
17	17.55	16	455	5	9	16.46
18	56.14	65	1086	34	50	29.86
19	33.82	31	734	5	16	29.71
20	60.68	65	1153	18	43	29.62
21	56.84	61	1049	26	42	29.16
22	70.00	78	1166	20	50	32.69
23	68.16	63	1260	20	40	29.94
24	51.43	57	1095	14	16	33.93
25	21.36	26	481	6	6	29.78
26	14.88	12	373	2	3	29.18
27	40.65	38	885	18	21	34.76
28	51.97	54	1041	14	17	37.31
29	72.42	78	1406	12	16	33.45
30	67.30	61	1250	8	12	36.68
31	44.76	41	997	12	23	22.97
32	35.35	28	732	10	17	23.67
33	60.17	57	1127	14	29	26.92
34	27.12	30	625	13	21	24.98
35	36.09	46	928	6	8	24.99
36	15.96	15	407	3	4	24.17
37	17.24	23	396	5	6	26.97
38	36.69	31	914	6	7	30.55
39	39.35	40	923	6	7	30.32
40	54.03	52	1283	7	9	30.58
41	19.24	18	509	2	3	26.37
42	18.99	21	522	3	3	27.57
43	7.54	11	188	4	4	27.24
44	34.38	30	753	6	6	24.14
45	30.88	32	629	9	9	25.11
46	25.52	28	584	7	10	28.22
47	58.91	66	1263	15	18	32.10
48	48.91	49	1113	7	9	33.27
49	15.69	13	411	4	4	26.85
...	...	...	...	...	...	...
3953	68.15	73	1276	23	38	28.97
3954	96.29	100	1616	38	68	35.08
3955	58.85	55	1053	20	38	34.04
3956	76.79	75	1443	27	56	33.07
3957	87.97	84	1659	28	61	31.56
3958	80.21	70	1484	27	52	31.84
3959	66.11	65	1276	23	42	28.74
3960	36.49	40	706	8	23	28.16
3961	39.92	46	922	14	19	28.38
3962	14.17	19	362	4	6	20.22
3963	26.27	32	615	5	7	30.87
3964	90.14	101	1715	34	45	34.60
3965	89.65	90	1909	20	36	37.25
3966	34.43	40	907	15	29	21.76
3967	23.48	27	493	6	16	21.26
3968	14.95	10	368	4	7	20.99
3969	32.90	37	753	17	24	20.70
3970	31.14	27	721	12	19	19.72
3971	6.20	8	139	2	5	17.76
3972	39.32	44	998	12	26	23.30
3973	30.04	19	734	9	13	24.95
3974	41.49	38	1151	9	22	25.61
3975	11.61	4	328	1	3	19.87
3976	52.47	61	1214	10	35	25.72
3977	54.27	47	1155	16	32	27.30
3978	48.06	37	1070	14	19	27.81
3979	14.90	18	396	7	9	12.83
3980	6.39	6	177	2	5	14.59
3981	12.18	9	390	1	3	13.15
3982	13.53	11	385	5	6	15.13
3983	15.22	18	449	2	7	19.20
3984	49.77	54	1112	10	12	35.56
3985	25.19	28	576	2	2	34.78
3986	37.45	37	928	8	10	34.66
3987	32.46	42	798	5	7	28.52
3988	32.46	42	798	5	7	28.52
3989	98.18	117	2136	38	50	41.09
3990	56.59	55	1211	10	17	40.98
3991	92.10	97	1891	21	38	40.67
3992	61.34	61	1417	11	19	38.13
3993	73.24	88	1698	28	37	39.90
3994	78.53	88	1547	19	29	38.90
3995	71.75	77	1460	16	23	38.20
3996	39.29	36	810	14	22	25.32
3997	46.53	41	783	14	29	31.44
3998	36.56	31	887	11	18	24.95
3999	30.90	22	733	9	16	22.02
4000	28.77	25	739	8	15	23.17
4001	11.02	9	328	2	5	13.27
4002	7.84	4	191	0	0	14.30

4003 rows × 6 columns

X1 = df[['xGF', 'GF', 'CF', 'A', 'P', 'TOI%']]
y1 = df['cap_hit']

X1_train, X1_test, y1_train, y1_test = train_test_split(X1,
                                                        y1,
                                                        test_size=.2,
                                                        random_state=42)

lr1 = LinearRegression()

kf1 = KFold(n_splits = 5, shuffle=True, random_state=42)

scores = cross_val_score(lr1, X1_train, y1_train, cv=kf1)
print(scores)
print(scores.mean())

[0.39561726 0.44179173 0.45722054 0.40264518 0.4199964 ]
0.4234542231837091

lr1.fit(X1_train, y1_train)
lr1.score(X1_train, y1_train)

0.4253117997674285

lr1.score(X1_test, y1_test)

0.43392851955437783

Not really the greatest score, but I didn’t expect it to be that easy! Going to give these features another go using RandomForestRegressor and GridSearchCV.

Going to give these features another go using RandomForestRegressor.

# Instantiate
rf1 = RandomForestRegressor()

# I tried to be a badass and use something new I learned,
# but this took forever to run and I simply didn't have the time to let it finish
# Going to skip right to GridSearch
# # Use RandomizedSearchCV to try to get a sense
# # of the best ranges of parameters to feed into GridSearch
# n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
# max_features = ['auto', 'sqrt']
# max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]
# max_depth.append(None)
# min_samples_split = [2, 5, 10]
# min_samples_leaf = [1, 2, 4]
# bootstrap = [True, False]

# random_parameters = {'n_estimators': n_estimators,
#                      'max_features': max_features,
#                      'max_depth': max_depth,
#                      'min_samples_split': min_samples_split,
#                      'min_samples_leaf': min_samples_leaf,
#                      'bootstrap': bootstrap}

# rf1_ran = RandomizedSearchCV(estimator=rf1,
#                             param_distributions=random_parameters,
#                             n_iter=200,
#                             cv=5,
#                             random_state=42,
#                             n_jobs=-1)

# rf1_ran.fit(X1, y1)

params = {
    'bootstrap': [True],
    'max_depth': [80, 90, 100, 110],
    'max_features': [2, 3],
    'min_samples_leaf': [3, 4, 5],
    'min_samples_split': [8, 10, 12],
    'n_estimators': [100, 200, 300]
}

# Instantiate
GS1 = GridSearchCV(estimator = rf1,
                           param_grid = params,
                           cv = 3,
                           n_jobs = -1,
                           verbose = 2)

# Fit
GS1.fit(X1_train, y1_train)

Fitting 3 folds for each of 216 candidates, totalling 648 fits
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100


[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    9.2s


[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200


[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   44.7s


[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.3s
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.7s
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   0.9s
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100


[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  1.7min


[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.4s
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.8s
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.7s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s


[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  3.0min


[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.4s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.2s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.1s


[Parallel(n_jobs=-1)]: Done 648 out of 648 | elapsed:  3.0min finished





GridSearchCV(cv=3, error_score='raise',
       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
           oob_score=False, random_state=None, verbose=0, warm_start=False),
       fit_params=None, iid=True, n_jobs=-1,
       param_grid={'bootstrap': [True], 'max_depth': [80, 90, 100, 110], 'max_features': [2, 3], 'min_samples_leaf': [3, 4, 5], 'min_samples_split': [8, 10, 12], 'n_estimators': [100, 200, 300]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
       scoring=None, verbose=2)

GS1.best_params_

{'bootstrap': True,
 'max_depth': 80,
 'max_features': 3,
 'min_samples_leaf': 3,
 'min_samples_split': 8,
 'n_estimators': 300}

best_est = GS1.best_estimator_
best_est

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,
           max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=3,
           min_samples_split=8, min_weight_fraction_leaf=0.0,
           n_estimators=300, n_jobs=1, oob_score=False, random_state=None,
           verbose=0, warm_start=False)

GS1.best_score_

0.447463223036263

# Just copy and pasting from above
rf1_gs = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,
           max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=3,
           min_samples_split=8, min_weight_fraction_leaf=0.0,
           n_estimators=300, n_jobs=1, oob_score=False, random_state=None,
           verbose=0, warm_start=False)

rf1_gs.fit(X1_train, y1_train)

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,
           max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=3,
           min_samples_split=8, min_weight_fraction_leaf=0.0,
           n_estimators=300, n_jobs=1, oob_score=False, random_state=None,
           verbose=0, warm_start=False)

rf1_gs.score(X1_test, y1_test)

0.4506184437556441

These features clearly aren’t enough. Let’s try more variables. Next step after that will be standard scaling and/or Lasso.

Model 2: Everything

For my second model, I’m going to use all the features I have, to see if that adds any predictive value.

X2 = df.drop(axis=1, columns=['Player', 'Season', 'Team', 'Position','cap_hit'])
y2 = df['cap_hit']

X2_train, X2_test, y2_train, y2_test = train_test_split(X2,
                                                        y2,
                                                        test_size=.2,
                                                        random_state=42)

lr2 = LinearRegression(fit_intercept=True)

kf2 = KFold(n_splits = 5, shuffle=True, random_state=42)

scores = cross_val_score(lr2, X2_train, y2_train, cv=kf2)
print(scores)
print(scores.mean())

[0.42315116 0.47946384 0.48462904 0.42862296 0.46273087]
0.4557195737297571

lr2.fit(X2_train, y2_train)
lr2.score(X2_train, y2_train)

0.4677559076090967

lr2.score(X2_test, y2_test)

0.46737056229432883

Slightly better, but barely. Let’s see if GridSearch + Random Forest can save us from this mediocrity.

# Instantiate
rf2 = RandomForestRegressor()

params = {
    'bootstrap': [True],
    'max_depth': [80, 90, 100, 110],
    'max_features': [2, 3],
    'min_samples_leaf': [3, 4, 5],
    'min_samples_split': [8, 10, 12],
    'n_estimators': [100, 200, 300]
}

# Instantiate
GS2 = GridSearchCV(estimator = rf2,
                           param_grid = params,
                           cv = 3,
                           n_jobs = -1,
                           verbose = 2)

# Fit
GS2.fit(X2_train, y2_train)

Fitting 3 folds for each of 216 candidates, totalling 648 fits
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100


[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    9.3s


[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   2.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   2.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   2.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.4s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.5s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   2.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   2.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   2.1s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.2s
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200


[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   48.4s


[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.1s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.4s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=90, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.1s
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100


[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  1.9min


[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.8s
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.8s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   1.4s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   2.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   2.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   2.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=100, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   1.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   2.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   2.6s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   2.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.4s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.8s
[CV] bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   0.9s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=2, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.2s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=200, total=   1.3s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.9s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=200, total=   1.2s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=10, n_estimators=300, total=   1.8s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=3, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=8, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.7s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=100, total=   0.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=4, min_samples_split=12, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=8, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.6s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=100, total=   0.5s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200


[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  3.3min


[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=10, n_estimators=300, total=   1.7s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.0s
[CV] bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=200, total=   1.1s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.5s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.3s
[CV]  bootstrap=True, max_depth=110, max_features=3, min_samples_leaf=5, min_samples_split=12, n_estimators=300, total=   1.2s


[Parallel(n_jobs=-1)]: Done 648 out of 648 | elapsed:  3.3min finished





GridSearchCV(cv=3, error_score='raise',
       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
           oob_score=False, random_state=None, verbose=0, warm_start=False),
       fit_params=None, iid=True, n_jobs=-1,
       param_grid={'bootstrap': [True], 'max_depth': [80, 90, 100, 110], 'max_features': [2, 3], 'min_samples_leaf': [3, 4, 5], 'min_samples_split': [8, 10, 12], 'n_estimators': [100, 200, 300]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
       scoring=None, verbose=2)

GS2.best_params_

{'bootstrap': True,
 'max_depth': 80,
 'max_features': 3,
 'min_samples_leaf': 3,
 'min_samples_split': 8,
 'n_estimators': 200}

best_est = GS2.best_estimator_
best_est

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,
           max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=3,
           min_samples_split=8, min_weight_fraction_leaf=0.0,
           n_estimators=200, n_jobs=1, oob_score=False, random_state=None,
           verbose=0, warm_start=False)

GS2.best_score_

0.4524910072558504

# Just copy and pasting from above
rf2_gs = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,
           max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=3,
           min_samples_split=8, min_weight_fraction_leaf=0.0,
           n_estimators=300, n_jobs=1, oob_score=False, random_state=None,
           verbose=0, warm_start=False)

rf2_gs.fit(X1_train, y1_train)

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,
           max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=3,
           min_samples_split=8, min_weight_fraction_leaf=0.0,
           n_estimators=300, n_jobs=1, oob_score=False, random_state=None,
           verbose=0, warm_start=False)

rf2_gs.score(X1_test, y1_test)

0.45495103106520884

It’s not really much better. Let’s see if doing the same process, but scaling our variables works.

Model 3: StandardScalar + LassoCV

# Using all features again
X3 = df.drop(axis=1, columns=['Player', 'Season', 'Team', 'Position','cap_hit'])
y3 = df['cap_hit']

# Instantiate Scaler
ss3 = StandardScaler()

X3_train, X3_test, y3_train, y3_test = train_test_split(X3,
                                                        y3,
                                                        test_size=.2,
                                                        random_state=42)

X3_train_scaled = ss3.fit_transform(X3_train)
X3_test_scaled = ss3.transform(X3_test)

# # Can't get this to work, so skipping it
# params = {
#     'n_alphas': [100, 150, 200, 250],
#     'alphas': [.0001, .001, .01, .1, .5, 1, 5],
#     'max_iter': [250, 500, 1000],
#     'cv': [3, 5, 10]
#         }

# GS3 = GridSearchCV(estimator = LassoCV(),
#                            param_grid = params,
#                            cv = 3,
#                            n_jobs = -1,
#                            verbose = 2)

# # Fit
# GS3.fit(X3_train_scaled, y3_train)

lasso3 = LassoCV(n_alphas = 500, cv = 10)

lasso3_scores = cross_val_score(lasso3, X3_train_scaled, y3_train)
print(lasso3_scores)
print(lasso3_scores.mean())

[0.44149403 0.4549043  0.46311146]
0.4531699322322142

lasso3.fit(X3_train_scaled, y3_train)
lasso3.score(X3_test_scaled, y3_test)

0.4721485514611437

I’m starting to think that I may not be able to create good models based off of this data.

Model 4: StandardScalar + ElasticNetCV

# Using all features again
X4 = df.drop(axis=1, columns=['Player', 'Season', 'Team', 'Position','cap_hit'])
y4 = df['cap_hit']

# Instantiate Scaler
ss4 = StandardScaler()

X4_train, X4_test, y4_train, y4_test = train_test_split(X4,
                                                        y4,
                                                        test_size=.2,
                                                        random_state=42)

X4_train_scaled = ss4.fit_transform(X4_train)
X4_test_scaled = ss4.transform(X4_test)

l1_ratios = np.linspace(0.1, 1.0, num=25)
enet4 = ElasticNetCV(l1_ratio=l1_ratios, cv=10, n_alphas=250, max_iter=2500)

enet4_scores = cross_val_score(enet4, X4_train_scaled, y4_train, cv=10)
print(enet4_scores.mean())

0.45086930621743165

All of my models seem to come in at around the same score. I don’t think it’s possible to meaningfully predict salaries based on these poor scores.

Model 5: Polynomial Features + StandardScalar + LassoCV

# Using all features again
X5 = df.drop(axis=1, columns=['Player', 'Season', 'Team', 'Position','cap_hit'])
y5 = df['cap_hit']

poly5 = PolynomialFeatures(include_bias=False)

X5_poly = poly5.fit_transform(X5)

X5_train_poly, X5_test_poly, y5_train_poly, y5_test_poly = train_test_split(X5_poly, y5, test_size=.2, random_state=42)

# Instantiate Scaler
ss5 = StandardScaler()
X5_train_poly_scaled = ss5.fit_transform(X5_train_poly)
X5_test_poly_scaled = ss5.transform(X5_test_poly)

lasso_poly5 = LassoCV(verbose=1, max_iter=500)

lp5_scores = cross_val_score(lasso_poly5, X5_train_poly_scaled, y5_train_poly)
print(lp5_scores)
print(lp5_scores.mean())

......................................................................../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
............................................................................/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
...................................................................../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
.[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    3.2s finished
/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
............................................................................/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
............................................................................/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
......................................................................../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
..../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
.[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    3.3s finished
/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
...................................................................../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
.../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
.........................................................................../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
......................................................................../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
...../anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
./anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)
.[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    3.7s finished


[0.50027451 0.4926285  0.51313953]
0.5020141791735048


/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.
  ConvergenceWarning)

# Scores got lost in all the soft warnings:
# [0.50027451 0.4926285  0.51313953]
# 0.5020141791735048

Welp, looks like I wasn’t able to save hockey like Brad Pitt saved baseball.

Interpreting my Models

Because I really don’t know what I’m doing, I’m going to use my lr2 model.

preds = lr2.predict(df.drop(axis=1, columns=['Player', 'Season', 'Team', 'Position','cap_hit']))

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-311-78456c65b91e> in <module>
----> 1 preds = lr2.predict(df.drop(axis=1, columns=['Player', 'Season', 'Team', 'Position','cap_hit']))


/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/base.py in predict(self, X)
    254             Returns predicted values.
    255         """
--> 256         return self._decision_function(X)
    257
    258     _preprocess_data = staticmethod(_preprocess_data)


/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/base.py in _decision_function(self, X)
    239         X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
    240         return safe_sparse_dot(X, self.coef_.T,
--> 241                                dense_output=True) + self.intercept_
    242
    243     def predict(self, X):


/anaconda3/lib/python3.6/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a, b, dense_output)
    138         return ret
    139     else:
--> 140         return np.dot(a, b)
    141
    142


ValueError: shapes (4003,39) and (38,) not aligned: 39 (dim 1) != 38 (dim 0)

df.shape

(4003, 43)

preds.shape

(4003,)

y1.shape

(4003,)

df['cap_hit'].values

array([1775000,  925000,  925000, ..., 1600000,  700000,  925000])

df['predicted_cap_hit'] = [pd.Series(preds) - pd.Series(df['cap_hit'].values)]

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-303-8487aa207e6b> in <module>
----> 1 df['predicted_cap_hit'] = [pd.Series(preds) - pd.Series(df['cap_hit'].values)]


/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in __setitem__(self, key, value)
   3117         else:
   3118             # set column
-> 3119             self._set_item(key, value)
   3120
   3121     def _setitem_slice(self, key, value):


/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in _set_item(self, key, value)
   3192
   3193         self._ensure_valid_index(value)
-> 3194         value = self._sanitize_column(key, value)
   3195         NDFrame._set_item(self, key, value)
   3196


/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in _sanitize_column(self, key, value, broadcast)
   3389
   3390             # turn me into an ndarray
-> 3391             value = _sanitize_index(value, self.index, copy=False)
   3392             if not isinstance(value, (np.ndarray, Index)):
   3393                 if isinstance(value, list) and len(value) > 0:


/anaconda3/lib/python3.6/site-packages/pandas/core/series.py in _sanitize_index(data, index, copy)
   3999
   4000     if len(data) != len(index):
-> 4001         raise ValueError('Length of values does not match length of ' 'index')
   4002
   4003     if isinstance(data, ABCIndexClass) and not copy:


ValueError: Length of values does not match length of index

df['predicted_cap_hit'] = preds

df['preds_actual'] = preds - df['cap_hit'].values

df['actual_preds'] = df['cap_hit'].values - preds

df['preds_actual'].sort_values().head(10)

 -6.403421e+06
  -5.597727e+06
 -5.501275e+06
 -5.488481e+06
  -5.273529e+06
  -5.221957e+06
 -5.166968e+06
 -5.156936e+06
  -4.971319e+06
 -4.844826e+06
Name: preds_actual, dtype: float64

df['actual_preds'].sort_values().head(10)

 -6.191788e+06
  -5.663629e+06
  -4.931784e+06
  -4.683279e+06
 -4.606852e+06
 -4.587498e+06
 -4.396056e+06
 -4.167917e+06
  -4.087091e+06
 -4.067525e+06
Name: actual_preds, dtype: float64

list(df['preds_actual'].sort_values().head(10).index)

[1942, 396, 3403, 1706, 323, 128, 1943, 1034, 150, 1944]

list(df['actual_preds'].sort_values().head(10).index)

Int64Index([1356, 909, 908, 138, 2268, 1905, 1937, 1432, 333, 2785], dtype='int64')

# Most undervalued players:
df.iloc[df['actual_preds'].sort_values().head(10).index]

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	iSh%	PDO	ZSR	TOI%	CF% QoT	CF% QoC	cap_hit	predicted_cap_hit	preds_actual	actual_preds
1356	Erik Karlsson	2011-2012	OTT	D	81	2050.66	19	58	77	55	...	7.28	101.52	64.94	41.64	54.46	45.68	875000	7.066788e+06	6.191788e+06	-6.191788e+06
909	Connor Mcdavid	2017-2018	EDM	C	82	1765.89	41	66	107	79	...	14.91	100.42	61.02	35.51	52.53	47.40	925000	6.588629e+06	5.663629e+06	-5.663629e+06
908	Connor Mcdavid	2016-2017	EDM	C	82	1732.77	30	70	100	74	...	11.90	103.19	62.81	34.71	53.10	46.89	925000	5.856784e+06	4.931784e+06	-4.931784e+06
138	Alex Pietrangelo	2011-2012	STL	D	81	2001.81	12	37	49	32	...	5.94	101.77	48.87	40.59	51.81	51.36	816666	5.499945e+06	4.683279e+06	-4.683279e+06
2268	Leon Draisaitl	2016-2017	EDM	C	82	1548.48	29	48	77	60	...	16.76	102.10	61.23	31.06	55.67	45.96	925000	5.531852e+06	4.606852e+06	-4.606852e+06
1905	John Tavares	2011-2012	NYI	C	82	1686.61	31	50	81	67	...	10.84	99.79	67.52	33.82	48.07	45.25	900000	5.487498e+06	4.587498e+06	-4.587498e+06
1937	Jonathan Marchessault	2017-2018	VGK	C	77	1347.85	27	47	74	60	...	10.11	104.52	61.75	28.92	54.42	45.73	750000	5.146056e+06	4.396056e+06	-4.396056e+06
1432	Gabriel Landeskog	2013-2014	COL	L	81	1512.34	26	39	65	54	...	11.71	103.36	53.25	30.72	50.93	46.68	925000	5.092917e+06	4.167917e+06	-4.167917e+06
333	Artemi Panarin	2015-2016	CHI	C	80	1480.87	30	47	77	59	...	16.04	103.20	81.40	30.63	55.79	44.70	812500	4.899591e+06	4.087091e+06	-4.087091e+06
2785	Mikko Rantanen	2017-2018	COL	R	81	1535.76	29	55	84	58	...	16.29	103.05	66.95	31.31	52.65	44.03	894167	4.961692e+06	4.067525e+06	-4.067525e+06

10 rows × 46 columns

# Most overvalued players:
df.iloc[df['preds_actual'].sort_values().head(10).index]

	Player	Season	Team	Position	GP	TOI	G	A	P	P1	...	iSh%	PDO	ZSR	TOI%	CF% QoT	CF% QoC	cap_hit	predicted_cap_hit	preds_actual	actual_preds
1942	Jonathan Toews	2015-2016	CHI	C	80	1538.72	27	29	56	44	...	15.25	102.79	58.34	31.76	55.15	48.89	10500000	4.096579e+06	-6.403421e+06	6.403421e+06
396	Bobby Ryan	2016-2017	OTT	R	62	963.34	13	12	25	21	...	11.71	101.21	59.10	25.77	55.20	45.08	7250000	1.652273e+06	-5.597727e+06	5.597727e+06
3403	Scott Gomez	2011-2012	MTL	C	38	537.09	2	9	11	10	...	3.39	96.74	69.57	23.40	53.28	44.56	7357143	1.855868e+06	-5.501275e+06	5.501275e+06
1706	Jason Spezza	2017-2018	DAL	C	78	1014.48	8	18	26	21	...	5.80	98.14	62.85	21.83	55.86	44.45	7500000	2.011519e+06	-5.488481e+06	5.488481e+06
323	Anze Kopitar	2016-2017	L.A	C	76	1578.27	12	39	51	34	...	8.00	97.93	53.61	34.25	56.87	48.69	10000000	4.726471e+06	-5.273529e+06	5.273529e+06
128	Alex Ovechkin	2011-2012	WSH	L	78	1543.41	38	27	65	52	...	12.54	101.85	65.34	32.66	54.24	43.75	9538462	4.316505e+06	-5.221957e+06	5.221957e+06
1943	Jonathan Toews	2016-2017	CHI	C	72	1451.09	21	37	58	42	...	10.55	100.86	55.43	33.22	53.01	48.53	10500000	5.333032e+06	-5.166968e+06	5.166968e+06
1034	Dany Heatley	2013-2014	MIN	L	76	1126.21	12	16	28	24	...	10.91	99.44	63.26	24.45	56.45	44.45	7500000	2.343064e+06	-5.156936e+06	5.156936e+06
150	Alex Semin	2014-2015	CAR	R	57	907.66	6	13	19	13	...	6.45	97.20	64.71	26.35	56.00	46.08	7000000	2.028681e+06	-4.971319e+06	4.971319e+06
1944	Jonathan Toews	2017-2018	CHI	C	74	1457.10	20	31	51	41	...	9.48	98.35	57.20	32.58	54.64	48.07	10500000	5.655174e+06	-4.844826e+06	4.844826e+06

10 rows × 46 columns

For closing thoughts / next steps: NHL deputy commissioner Bill Daly confirmed to theScore that a full rollout of player and puck tracking is penciled in to debut during the 2019-20 season.

Capstone: Gauging what determines player value in the NHL

Introduction

NHL Advanced Stats

Imports

Data Collection

NHL Salary Cap

Scraper Code - Part I - Player Performance Data

Relevant URLs that I’ll be pulling from.

Pull request, status code check, and transform into BeautifulSoup

Scraper Code - Part II - Player Cap Hits

Cleaning up the Cap Hit Data

Data Cleaning & Combining

Part I - Combining player performance and cap hit

Explaining the different play situations

Part II - Adding in Team stats

Merge in with Main Data Set

Explaining Important Advanced Metrics

Important Assumption

Key Advanced Metrics

EDA

Create Models

Model 1: Highest-correlation in features (r-score > .55)

Model 2: Everything

Model 3: StandardScalar + LassoCV

Model 4: StandardScalar + ElasticNetCV

Model 5: Polynomial Features + StandardScalar + LassoCV

Interpreting my Models