Python机器学习之梯度提升树

#和随机森林一样，基于决策树，采用连续的方式构建树，深度很小max_depth<5.重要的参数n_estimate和learning_rate,这两个参数的y作用在于对模型过拟合化得调整，从而提高模型得泛化能力。

from sklearn.ensemble import GradientBoostingClassifier

from sklearn.datasets import load_breast_cancer

from sklearn.model_selection import train_test_split

cancer=load_breast_cancer()

x_train,x_test,y_train,y_test=train_test_split(cancer.data,cancer.target,random_state=0)

gbrt=GradientBoostingClassifier()#模型不做参数调整

gbrt.fit(x_train,y_train)

print(gbrt.score(x_train,y_train))

print(gbrt.score(x_test,y_test))

#对模型做预剪枝

gbrt=GradientBoostingClassifier(n_estimate=100,learning_rate=0.01)

#n_estimate主要控制树的数量，learning_rate控制错误的纠正度改参数越小模型越复杂