<jupyter_start><jupyter_text>Decision Tree<jupyter_code>run_model( DecisionTreeRegressor(max_depth=5), cat_feats )<jupyter_output><empty_output><jupyter_text>Random Forest<jupyter_code>model = RandomForestRegressor(max_depth=5, n_estimators=50, random_state=0)
run_model(model, cat_feats)<jupyter_output><empty_output><jupyter_text>XGBoost<jupyter_code>xgb_params = {
    'max_depth' : 5 ,
    'n_estimators' : 50,
    'learning_rate': 0.1,
    'seed': 0
}


run_model(xgb.XGBRegressor(**xgb_params), cat_feats)
m = xgb.XGBRegressor(max_depth=5, n_estimators=50, learning_rate=0.1, seed = 0)
m.fit(X,y)
imp = PermutationImportance(m, random_state=0).fit(X,y)

eli5.show_weights(imp, feature_names = cat_feats)
feats = ['param_napęd__cat','param_rok-produkcji__cat','param_stan__cat','param_skrzynia-biegów__cat','param_faktura-vat__cat','param_moc__cat','param_marka-pojazdu__cat','feature_kamera-cofania__cat','param_typ__cat','param_pojemność-skokowa__cat','seller_name__cat','feature_wspomaganie-kierownicy__cat','param_model-pojazdu__cat','param_wersja__cat','param_kod-silnika__cat','feature_system-start-stop__cat','feature_asystent-pasa-ruchu__cat','feature_czujniki-parkowania-przednie__cat','feature_łopatki-zmiany-biegów__cat','feature_regulowane-zawieszenie__cat']
len(feats)
run_model(xgb.XGBRegressor(**xgb_params), feats)
df['param_rok-produkcji'].map(lambda x: -1 if str(x) == "None" else int(x))
df['param_rok-produkcji'] = df['param_rok-produkcji'].map(lambda x: -1 if str(x) == "None" else int(x))
feats = ['param_napęd__cat','param_rok-produkcji','param_stan__cat','param_skrzynia-biegów__cat','param_faktura-vat__cat','param_moc__cat','param_marka-pojazdu__cat','feature_kamera-cofania__cat','param_typ__cat','param_pojemność-skokowa__cat','seller_name__cat','feature_wspomaganie-kierownicy__cat','param_model-pojazdu__cat','param_wersja__cat','param_kod-silnika__cat','feature_system-start-stop__cat','feature_asystent-pasa-ruchu__cat','feature_czujniki-parkowania-przednie__cat','feature_łopatki-zmiany-biegów__cat','feature_regulowane-zawieszenie__cat']
len(feats)
run_model(xgb.XGBRegressor(**xgb_params), feats)
df['param_moc'] = df['param_moc'].map(lambda x: -1 if str(x) == 'None' else int(x.split(' ')[0]) )
feats = ['param_napęd__cat','param_rok-produkcji','param_stan__cat','param_skrzynia-biegów__cat','param_faktura-vat__cat','param_moc','param_marka-pojazdu__cat','feature_kamera-cofania__cat','param_typ__cat','param_pojemność-skokowa__cat','seller_name__cat','feature_wspomaganie-kierownicy__cat','param_model-pojazdu__cat','param_wersja__cat','param_kod-silnika__cat','feature_system-start-stop__cat','feature_asystent-pasa-ruchu__cat','feature_czujniki-parkowania-przednie__cat','feature_łopatki-zmiany-biegów__cat','feature_regulowane-zawieszenie__cat']
len(feats)
run_model(xgb.XGBRegressor(**xgb_params), feats)
df['param_pojemność-skokowa'].unique()
df['param_pojemność-skokowa'].map(lambda x: -1 if str(x) == 'None' else int(x.split('cm')[0].replace(' ', (''))) )
df['param_pojemność-skokowa'] = df['param_pojemność-skokowa'].map(lambda x: -1 if str(x) == 'None' else int( str(x).split('cm')[0].replace(' ', (''))) )

feats = ['param_napęd__cat','param_rok-produkcji','param_stan__cat','param_skrzynia-biegów__cat','param_faktura-vat__cat','param_moc','param_marka-pojazdu__cat','feature_kamera-cofania__cat','param_typ__cat','param_pojemność-skokowa','seller_name__cat','feature_wspomaganie-kierownicy__cat','param_model-pojazdu__cat','param_wersja__cat','param_kod-silnika__cat','feature_system-start-stop__cat','feature_asystent-pasa-ruchu__cat','feature_czujniki-parkowania-przednie__cat','feature_łopatki-zmiany-biegów__cat','feature_regulowane-zawieszenie__cat']
run_model(xgb.XGBRegressor(**xgb_params), feats)
!git config --global user.email "<EMAIL>"
!git config --global user.name "<NAME>"
!git add day4.ipynb
!git commit -m 'xgboost traning to imporve model'
!git push -u origin master<jupyter_output><empty_output>
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