评价指标
根据模型预测值和真实值的区别来评价模型。
1.回归
1.1 MAE
平均绝对误差，MAE，Mean Absolute Error
$$ MAE(y,\hat{y})=\frac{1}{n}\sum_{i=1}^n|y_i-\hat{y}_i| $$
1.2 MAPE
平均绝对百分比误差，MAPE，Mean Absolute Percentage Error
$$ MAPE(y,\hat{y})=\frac{1}{n}\sum_{i=1}^n\frac{|y_i-\hat{y}_i|}{|y_i|} $$
1.3 MSE
均方误差，MSE，Mean Squared Error
$$ MSE(y,\hat{y})=\frac{1}{n}\sum_{i=1}^n|y_i-\hat{y}_i|_2^2 $$
1.4 RMSE
均方根误差，RMSE，Root Mean Squared Error
$$ RMSE(y,\hat{y})=\sqrt{\frac{1}{n}\sum_{i=1}^n|y_i-\hat{y}_i|_2^2} $$
1.5 MSLE
均方误差对数，MSLE，Mean Squared Log Error
$$ MSLE(y,\hat{y})=\frac{1}{n}\sum_{i=1}^n\big(\log{(1+y_i)}-\log{(1+\hat{y}_i)}\big)^2 $$
1.6 MedAE
中位绝对误差，MedAE，Median Absolute Error
$$ MedAE(y,\hat{y})=median(|y_1-\hat{y}_1|,\cdots,|y_n-\hat{y}_n|) $$
1.7 $R^2$
拟合优度/可决系数，$R^2$，R Squared
$$ R^2(y,\hat{y})=1-\frac{ \sum_{i=1}^n (y_i-\hat{y_i})^2 }{ \sum_{i=1}^n (y_i-\bar{y})^2 } $$