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