sklearn：GBDT

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一、GBDT分类

（1）模型参数初始化：

from sklearn.ensemble import GradientBoostingClassifier

gbdt = GradientBoostingClassifier(    init=None,    learning_rate=0.1,    loss='deviance',    max_depth=3,    max_features=None,    max_leaf_nodes=None,    min_samples_leaf=1,    min_samples_split=2,    min_weight_fraction_leaf=0.0,    n_estimators=100,    random_state=None,    subsample=1.0,    verbose=0,    warm_start=False)

（2）训练：X,y为训练集

gbdt.fit(X, y)
（3）据此选重要特征，注：GBDT可以用来进行特征选择
score = gbdt.feature_importances_for s in score:  print s
（4）预测，看GBDT的分类效果
result = gbdt.predict(new_test_frature)
overall_accuracy = metrics.accuracy_score(result, y_test)print overall_accuracy

二、GBDT回归

（1）模型参数初始化：

from sklearn.ensemble import GradientBoostingRegressor

gbdt = GradientBoostingRegressor(    loss='ls',    learning_rate=0.1,    n_estimators=100,    subsample=1,    min_samples_split=2,    min_samples_leaf=1,    max_depth=3,    init=None,    random_state=None,    max_features=None,    alpha=0.9,    verbose=0,    max_leaf_nodes=None,    warm_start=False)
（2）训练：X,y为训练集
gbdt.fit(X, y)
（3）据此选重要特征
score = gbdt.feature_importances_for s in score:  print s
（4）预测，看GBDT的分类效果
result = gbdt.predict(new_test_frature)

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