python实现K近邻回归,采用等权重和不等权重的方法
如下所示：
fromsklearn.datasetsimportload_boston boston=load_boston() fromsklearn.cross_validationimporttrain_test_split importnumpyasnp; X=boston.data y=boston.target X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33,test_size=0.25) print'Themaxtargetvalueis:',np.max(boston.target) print'Themintargetvalueis:',np.min(boston.target) print'Theaveragetergetvalueis:',np.mean(boston.target) fromsklearn.preprocessingimportStandardScaler ss_X=StandardScaler() ss_y=StandardScaler() X_train=ss_X.fit_transform(X_train) X_test=ss_X.transform(X_test) y_train=ss_y.fit_transform(y_train) y_test=ss_y.transform(y_test) fromsklearn.neighborsimportKNeighborsRegressor uni_knr=KNeighborsRegressor(weights='uniform') uni_knr.fit(X_train,y_train) uni_knr_y_predict=uni_knr.predict(X_test) dis_knr=KNeighborsRegressor(weights='distance') dis_knr.fit(X_train,y_train) dis_knr_y_predict=dis_knr.predict(X_test) fromsklearn.metricsimportr2_score,mean_squared_error,mean_absolute_error print'R-squaredvalueofuniformweightsKNeighorRegressoris:',uni_knr.score(X_test,y_test) print'ThemeansquarederrorofuniformweightsKNeighorRegressoris:',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(uni_knr_y_predict)) print'ThemeanabsoluteerrorofuniformweightsKNeighorRegressoris:',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(uni_knr_y_predict)) print'R-squaredofdistanceweightsKNeighorRegressoris:',dis_knr.score(X_test,y_test) print'thevalueofmeansquarederrorofdistanceweightsKNeighorRegressoris:',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(dis_knr_y_predict)) print'thevalueofmeanssbsoluteerrorofdistanceweightsKNeighorRegressoris:',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(dis_knr_y_predict))
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