# -*- coding: utf-8 -*- ''' Created on 2018年1月24日 @author: Jason.F @summary: 有監督回歸學習-多元線性回歸的性能評估 ''' import pandas as pd import numpy as np import time import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.cross_validation import train_test_split from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score if __name__ == "__main__": start = time.clock() #導入波士頓房屋數據集df=pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data',header=None,sep='\s+')df.columns=['CRIM','ZM','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT','MEDV']X=df.iloc[:,:-1].valuesy=df['MEDV'].values#房間價格X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)slr=LinearRegression()slr.fit(X_train,y_train)y_train_pred=slr.predict(X_train)y_test_pred = slr.predict(X_test)#可視化殘差值和預測值plt.scatter(y_train_pred,y_train_pred-y_train,c='blue',marker='o',label='Training data')plt.scatter(y_test_pred,y_test_pred-y_test,c='lightgreen',marker='s',label='Test data')plt.xlabel('Predicted values')plt.ylabel('Residuals')plt.legend(loc='upper left')plt.hlines(y=0,xmin=-10,xmax=50,lw=2,colors='red')plt.xlim([-10,50])plt.show()#評估均方誤差print ('MSE train: %.3f,test:%.3f' % (mean_squared_error(y_train,y_train_pred),mean_squared_error(y_test,y_test_pred)))#評估決定系數(coefficient of determination)，是MSE的標準化print ('R^2 train: %.3f,test:%.3f' % (r2_score(y_train,y_train_pred),r2_score(y_test,y_test_pred)))end = time.clock() print('finish all in %s' % str(end - start))

結果：

MSE train: 19.958,test:27.196 R^2 train: 0.765,test:0.673 finish all in 12.8996295796

總結

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