如下所示：

from sklearn.datasets import load_boston boston = load_boston() from sklearn.cross_validation import train_test_split import numpy as np; X = boston.datay = 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))

以上這篇python實(shí)現(xiàn)K近鄰回歸,采用等權(quán)重和不等權(quán)重的方法就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持武林站長(zhǎng)站。