回帰問題の評価測定

https://blog.csdn.net/jasonding1354/article/details/46340729
分類問題では評価測定は精度であるが,この方法は回帰問題には適用されない.連続数値に対する評価測定(evaluation metrics)を用いた.
回帰問題に対する一般的な3つの評価測定を紹介する
In [21]:

# define true and predicted response values
true = [100, 50, 30, 20]
pred = [90, 50, 50, 30]

(1)平均絶対誤差(Mean Absolute Error,MAE)
 
(2)平均二乗誤差(Mean Squared Error,MSE)
 
(3)平均二乗誤差(Root Mean Squared Error,RMSE)
 
In [24]:

from sklearn import metrics
import numpy as np
# calculate MAE by hand
print "MAE by hand:",(10 + 0 + 20 + 10)/4.

# calculate MAE using scikit-learn
print "MAE:",metrics.mean_absolute_error(true, pred)

# calculate MSE by hand
print "MSE by hand:",(10**2 + 0**2 + 20**2 + 10**2)/4.

# calculate MSE using scikit-learn
print "MSE:",metrics.mean_squared_error(true, pred)


# calculate RMSE by hand
print "RMSE by hand:",np.sqrt((10**2 + 0**2 + 20**2 + 10**2)/4.)

# calculate RMSE using scikit-learn
print "RMSE:",np.sqrt(metrics.mean_squared_error(true, pred))

MAE by hand: 10.0
MAE: 10.0
MSE by hand: 150.0
MSE: 150.0
RMSE by hand: 12.2474487139
RMSE: 12.247448713