回归学习-近邻

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#导入数据
from sklearn.datasets import load_boston
boston = load_boston()
print(boston.DESCR)#数据分割
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, test_size=0.25, random_state=33)#分析回归目标值的差异
print('The max target value is:',np.max(boston.target))
print('The min target value is:',np.min(boston.target))
print('The mean target value is:',np.mean(boston.target))
print('\n')#数据标准化
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)#训练模型
from sklearn.neighbors import KNeighborsRegressor
uni_knn = KNeighborsRegressor(weights='uniform')
uni_knn.fit(X_train, y_train)
uni_y_predict = uni_knn.predict(X_test)dis_knn = KNeighborsRegressor(weights = 'distance')
dis_knn.fit(X_train, y_train)
dis_y_predict = dis_knn.predict(X_test)
#性能分析
from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error
print('The value of default measurement of uniform-weights KNeighorRegression is:',uni_knn.score(X_test, y_test))      #自带的评估模型
print('The value of R-squared of uniform-weights KNeighorRegression is',r2_score(y_test, uni_y_predict))             #
print('The mean_squared_error of uniform-weights KNeighorRegression is',mean_squared_error(y_test, uni_y_predict))   #均方误差
print('The mean_absolute_error of uniform-weights KNeighorRegression is',mean_absolute_error(y_test, uni_y_predict)) #平均绝对误差
print('\n')print('The value of default measurement of distance-weights KNeighorRegression is:',dis_knn.score(X_test, y_test))
print('The value of R-squared of distance-weights KNeighorRegression is',r2_score(y_test, dis_y_predict))
print('The mean_squared_error of distance-weights KNeighorRegression is',mean_squared_error(y_test, dis_y_predict))
print('The mean_absolute_error of distance-weights KNeighorRegression is',mean_absolute_error(y_test, dis_y_predict))
print('\n')

结果：

由输出结果可知，采用加权平均的方式回归房价具有更好的预测性能

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