1. 平均绝对误差:MAE(Mean Absolute Error)
2. 均方误差:MSE(Mean Squared Error)
3. 均方根误差:RMSE(Root Mean Squard Error)
4. 决定系数:R2(R-Square)
5. 校正决定系数(Adjusted R-Square)
from sklearn.metrics import mean_squared_error #均方误差
from sklearn.metrics import mean_absolute_error #平方绝对误差
from sklearn.metrics import r2_score#R square
#调用
# MAE:
mean_absolute_error(y_test,y_predict)
# MSE:
mean_squared_error(y_test,y_predict)
# RMSE:
np.sqrt(mean_squared_error(y_test,y_predict))
# R2:
r2_score(y_test,y_predict)
# Adjusted_R2:
1-((1-r2_score(y_test,y_predict))*(n-1))/(n-p-1)
参考:回归评价指标:MSE、RMSE、MAE、R2、Adjusted R2
标签:机器,R2,predict,回归,squared,error,test,评价,mean From: https://www.cnblogs.com/odesey/p/17142001.html