随机森林模型sklearn_sklearn随机森林调参小结

param_test2 = {'max_depth': range(3, 14, 2), 'min_samples_split': range(50, 201, 20)}

gsearch2 = GridSearchCV(

estimator=RandomForestClassifier(n_estimators=60, min_samples_leaf=20, max_features='sqrt',

oob_score=True, random_state=10),

param_grid=param_test2, scoring='roc_auc', iid=False, cv=5)

gsearch2.fit(X, y)

print(gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_)

"""

[mean: 0.79379, std: 0.02347, params: {'max_depth': 3, 'min_samples_split': 50},

mean: 0.79339, std: 0.02410, params: {'max_depth': 3, 'min_samples_split': 70},

mean: 0.79350, std: 0.02462, params: {'max_depth': 3, 'min_samples_split': 90},

mean: 0.79367, std: 0.02493, params: {'max_depth': 3, 'min_samples_split': 110},

mean: 0.79387, std: 0.02521, params: {'max_depth': 3, 'min_samples_split': 130},

mean: 0.79373, std: 0.02524, params: {'max_depth': 3, 'min_samples_split': 150},

mean: 0.79378, std: 0.02532, params: {'max_depth': 3, 'min_samples_split': 170},

mean: 0.79349, std: 0.02542, params: {'max_depth': 3, 'min_samples_split': 190},

mean: 0.80960, std: 0.02602, params: {'max_depth': 5, 'min_samples_split': 50},

mean: 0.80920, std: 0.02629, params: {'max_depth': 5, 'min_samples_split': 70},

mean: 0.80888, std: 0.02522, params: {'max_depth': 5, 'min_samples_split': 90},

mean: 0.80923, std: 0.02777, params: {'max_depth': 5, 'min_samples_split': 110},

mean: 0.80823, std: 0.02634, params: {'max_depth': 5, 'min_samples_split': 130},

mean: 0.80801, std: 0.02637, params: {'max_depth': 5, 'min_samples_split': 150},

mean: 0.80792, std: 0.02685, params: {'max_depth': 5, 'min_samples_split': 170},

mean: 0.80771, std: 0.02587, params: {'max_depth': 5, 'min_samples_split': 190},

mean: 0.81688, std: 0.02996, params: {'max_depth': 7, 'min_samples_split': 50},

mean: 0.81872, std: 0.02584, params: {'max_depth': 7, 'min_samples_split': 70},

mean: 0.81501, std: 0.02857, params: {'max_depth': 7, 'min_samples_split': 90},

mean: 0.81476, std: 0.02552, params: {'max_depth': 7, 'min_samples_split': 110},

mean: 0.81557, std: 0.02791, params: {'max_depth': 7, 'min_samples_split': 130},

mean: 0.81459, std: 0.02905, params: {'max_depth': 7, 'min_samples_split': 150},

mean: 0.81601, std: 0.02808, params: {'max_depth': 7, 'min_samples_split': 170},

mean: 0.81704, std: 0.02757, params: {'max_depth': 7, 'min_samples_split': 190},

mean: 0.82090, std: 0.02665, params: {'max_depth': 9, 'min_samples_split': 50},

mean: 0.81908, std: 0.02527, params: {'max_depth': 9, 'min_samples_split': 70},

mean: 0.82036, std: 0.02422, params: {'max_depth': 9, 'min_samples_split': 90},

mean: 0.81889, std: 0.02927, params: {'max_depth': 9, 'min_samples_split': 110},

mean: 0.81991, std: 0.02868, params: {'max_depth': 9, 'min_samples_split': 130},

mean: 0.81788, std: 0.02436, params: {'max_depth': 9, 'min_samples_split': 150},

mean: 0.81898, std: 0.02588, params: {'max_depth': 9, 'min_samples_split': 170},

mean: 0.81746, std: 0.02716, params: {'max_depth': 9, 'min_samples_split': 190},

mean: 0.82395, std: 0.02454, params: {'max_depth': 11, 'min_samples_split': 50},

mean: 0.82380, std: 0.02258, params: {'max_depth': 11, 'min_samples_split': 70},

mean: 0.81953, std: 0.02552, params: {'max_depth': 11, 'min_samples_split': 90},

mean: 0.82254, std: 0.02366, params: {'max_depth': 11, 'min_samples_split': 110},

mean: 0.81950, std: 0.02768, params: {'max_depth': 11, 'min_samples_split': 130},

mean: 0.81887, std: 0.02636, params: {'max_depth': 11, 'min_samples_split': 150},

mean: 0.81910, std: 0.02734, params: {'max_depth': 11, 'min_samples_split': 170},

mean: 0.81564, std: 0.02622, params: {'max_depth': 11, 'min_samples_split': 190},

mean: 0.82291, std: 0.02092, params: {'max_depth': 13, 'min_samples_split': 50},

mean: 0.82177, std: 0.02513, params: {'max_depth': 13, 'min_samples_split': 70},

mean: 0.82415, std: 0.02480, params: {'max_depth': 13, 'min_samples_split': 90},

mean: 0.82420, std: 0.02417, params: {'max_depth': 13, 'min_samples_split': 110},

mean: 0.82209, std: 0.02481, params: {'max_depth': 13, 'min_samples_split': 130},

mean: 0.81852, std: 0.02227, params: {'max_depth': 13, 'min_samples_split': 150},

mean: 0.81955, std: 0.02885, params: {'max_depth': 13, 'min_samples_split': 170},

mean: 0.82092, std: 0.02600, params: {'max_depth': 13, 'min_samples_split': 190}]

{'max_depth': 13, 'min_samples_split': 110}

0.8242016800050813

"""