Python实现Adaboost（decisiontree）

# -*- coding: utf-8 -*-"""Created on Thu Sep  7 17:17:22 2017@author: piaodexin"""from sklearn import datasetsfrom sklearn.svm import LinearSVCfrom sklearn import ensemblefrom sklearn.model_selection import validation_curveimport matplotlib.pyplot as pltimport numpy as npdata=datasets.load_digits()x=data.datay=data.targetestimator_1=LinearSVC()estimator_2=ensemble.AdaBoostClassifier(LinearSVC(),n_estimators=100,algorithm='SAMME')estimator_2.get_params().keys()validation_curve()n=np.linspace(0.1,1,20)train_score1,validation_score1=validation_curve(estimator_1,x,y,param_name='C',param_range=n,cv=3)train_score2,validation_score2=validation_curve(estimator_2,x,y,param_name='base_estimator__C',param_range=n,cv=3)n=np.linspace(0.1,1,20)plt.grid()plt.fill_between(n,train_score1.mean(1)-train_score1.std(1),                 train_score1.mean(1)+train_score1.std(1),color='r',alpha=0.1)plt.fill_between(n,validation_score1.mean(1)-validation_score1.std(1),                 validation_score1.mean(1)+validation_score1.std(1),color='g',alpha=0.1)plt.plot(n,train_score1.mean(1),c='r',label='train score')plt.plot(n,validation_score1.mean(1),c='g',label='validation score')plt.legend(loc='best')plt.show()plt.grid()plt.fill_between(n,train_score2.mean(1)-train_score2.std(1),                 train_score2.mean(1)+train_score2.std(1),color='r',alpha=0.1)plt.fill_between(n,validation_score2.mean(1)-validation_score2.std(1),                 validation_score2.mean(1)+validation_score2.std(1),color='g',alpha=0.1)plt.plot(n,train_score2.mean(1),c='r',label='train score')plt.plot(n,validation_score2.mean(1),c='g',label='validation score')plt.legend(loc='best')plt.show()