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@@ -3,12 +3,12 @@
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#-*- coding:utf-8 -*-
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import numpy as np
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-from sklearn.ensemble import GradientBoostingClassifier
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+from sklearn.ensemble import GradientBoostingClassifier,GradientBoostingRegressor
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from sklearn.model_selection import train_test_split
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-from sklearn.datasets import load_wine
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+from sklearn.datasets import load_wine,load_boston
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wine = load_wine()
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Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)
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-#print (X,y)
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+print(Ytrain)
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#默认参数
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#Accuracy : 0.9856
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@@ -16,5 +16,22 @@ Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=
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gbm1 = GradientBoostingClassifier( n_estimators=500,max_depth=10,max_features='sqrt', random_state=10)
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gbm1.fit(Xtrain,Ytrain)
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print("gbdt1",gbm1.score(Xtest,Ytest))
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-
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+
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+boston = load_boston()
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+Xtrain, Xtest, Ytrain, Ytest = train_test_split(boston.data, boston.target, test_size=0.3)
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+print(Ytrain)
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+
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+# 默认参数
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+# Accuracy : 0.9856
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+# AUC Score (Train): 0.862264
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+gbm2 = GradientBoostingRegressor(n_estimators=5, max_depth=5, max_features='sqrt', random_state=10)
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+gbm2.fit(Xtrain, Ytrain) # 分数越高越好
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+print("gbdt1", gbm2.score(Xtest, Ytest))
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+
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+gbm2 = GradientBoostingRegressor(n_estimators=50, max_depth=5, max_features='sqrt', random_state=10)
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+gbm2.fit(Xtrain, Ytrain)
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+print("gbdt2", gbm2.score(Xtest, Ytest))
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+
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+gbdt3 = GradientBoostingRegressor(n_estimators=150, max_depth=5, max_features='sqrt', random_state=10)
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+gbdt3.fit(Xtrain, Ytrain)
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+print("gbdt3", gbdt3.score(Xtest, Ytest))
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