python实现K近邻回归,采用等权重和不等权重的方法_Python

python实现K近邻回归,采用等权重和不等权重的方法

2021-05-21 00:23UESTC_C2_403 Python

今天小编就为大家分享一篇python实现K近邻回归,采用等权重和不等权重的方法，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

如下所示：

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									from sklearn.datasets import load_boston

									boston = load_boston()

									from sklearn.cross_validation import train_test_split

									import numpy as np;

									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 'The max target value is: ', np.max(boston.target)

									print 'The min target value is: ', np.min(boston.target)

									print 'The average terget value is: ', np.mean(boston.target)

									from sklearn.preprocessing import StandardScaler

									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)

									from sklearn.neighbors import KNeighborsRegressor

									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)

									from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error

									print 'R-squared value of uniform weights KNeighorRegressor is: ', uni_knr.score(X_test, y_test)

									print 'The mean squared error of uniform weights KNeighorRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))

									print 'The mean absolute error of uniform weights KNeighorRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))

									print 'R-squared of distance weights KNeighorRegressor is: ', dis_knr.score(X_test, y_test)

									print 'the value of mean squared error of distance weights KNeighorRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict))

									print 'the value of mean ssbsolute error of distance weights KNeighorRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict))