K近邻（回归）模型同样只是借助周围K个最近训练样本的目标数值，对待测样本的回归值进行决策。

平均回归

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)

距离加权回归

dis_knr = KNeighborsRegressor(weights='distance')
dis_knr.fit(X_train, y_train)
dis_knr_y_predict = dis_knr.predict(X_test)

使用R-squared、MSE、MAE三种指标对平均回归配置的K近邻模型在测试集上进行性能评估

print 'R-squared value of uniform-weighted KNeighorRegression:', uni_knr.score(X_test, y_test)
print 'The mean squared error of uniform-weighted KNeighorRegression:', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))
print 'The mean absoluate error of uniform-weighted KNeighorRegression', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))