from sklearn.datasets import load_boston
import numpy as np
boston = load_boston()
x = boston.data
y = boston.target
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)
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.reshape(-1, 1))
y_test = ss_y.transform(y_test.reshape(-1, 1))
from sklearn.neighbors import KNeighborsRegressor
uni_knr = KNeighborsRegressor(weights='uniform')
uni_knr.fit(x_train, y_train.ravel())
uni_knr_y_predict = uni_knr.predict(x_test)
dis_knr = KNeighborsRegressor(weights='distance')
dis_knr.fit(x_train, y_train.ravel())
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-weighted KNeighborRegression:', uni_knr.score(x_test, y_test))
print('The mean squared error of uniform-weighted KNeighborRegression:', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict)))
print('The mean absolute error of uniform-weighted KNeighborRegression', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict)))
print('\nR-squared value of uniform-weighted KNeighborRegression:', dis_knr.score(x_test, y_test))
print('The mean squared error of uniform-weighted KNeighborRegression:', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict)))
print('The mean absolute error of uniform-weighted KNeighborRegression', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict)))
運行結果如下：
R-squared value of uniform-weighted KNeighborRegression: 0.690345456461
The mean squared error of uniform-weighted KNeighborRegression: 24.0110141732
The mean absolute error of uniform-weighted KNeighborRegression 2.96803149606
R-squared value of uniform-weighted KNeighborRegression: 0.719758997016
The mean squared error of uniform-weighted KNeighborRegression: 21.7302501609
The mean absolute error of uniform-weighted KNeighborRegression 2.80505687851
