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：

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))

来源：https://blog.csdn.net/UESTC_C2_403/article/details/72848636