学习笔记：不同K近邻回归预测波士顿房价

代码如下：

# -*- coding: utf-8 -*-
"""
Created on Mon May 21 15:52:45 2018

@author: eagle
"""

# =============================================================================
# 不同K近邻回归预测波士顿房价
# =============================================================================

from sklearn.datasets import load_boston
from sklearn.cross_validation import train_test_split
import numpy as np
from sklearn.preprocessing import StandardScaler

from sklearn.neighbors import KNeighborsRegressor #K近邻回归器

from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error

#加载房价数据
boston = load_boston()      #共506条数据
print(boston.DESCR)
X = boston.data
y = boston.target


#训练数据和测试数据分割
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.25,random_state = 33)
#print('The max target value is',np.max(boston.target)) #最大值
#print('The min target value is',np.min(boston.target)) #最小值
#print('The average target value is',np.mean(boston.target)) #平均值

#标准化
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))

#不同预测方式
#平均回归
uni_knr=KNeighborsRegressor(weights='uniform')
uni_knr.fit(X_train,y_train)
uni_knr_y_predict=uni_knr.predict(X_test)

#按距离加权回归
dis_knr=KNeighborsRegressor(weights='distance')
dis_knr.fit(X_train,y_train)
dis_knr_y_predict=dis_knr.predict(X_test)

#评价
#平均回归
print('The scroe of uni_knr is:',uni_knr.score(X_test,y_test))
print('The r2-score of uni_knr is:',r2_score(y_test,uni_knr_y_predict))
print('The mean_squared_error of uni_knr is',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(uni_knr_y_predict)))
print('The mean_absolute_error of uni_knr is',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(uni_knr_y_predict)))

#按距离加权回归
print('The scroe of dis_knr is:',dis_knr.score(X_test,y_test))
print('The r2-score of dis_knr is:',r2_score(y_test,dis_knr_y_predict))
print('The mean_squared_error of dis_knr is',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(dis_knr_y_predict)))
print('The mean_absolute_error of dis_knr is',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(dis_knr_y_predict)))