Performance Measure for Regressi

均方误差—Mean Square Error(MSE)

参数估计值与参数真值之差平方的期望值
可以用于评价数据的变化程度，MSE值越小，预测模型描述实验数据具有更好的精确度

均方根误差—Root Mean Square Error(RMSE)

Even though the RMSE is generally the preferred performance measure for regression tasks, in some contexts you may prefer to use another function. For example, suppose that there are many outlier districts. In that case, you may consider using the Mean Absolute Error

平均绝对误差—Mean Absolute Error(MAE)

平均绝对误差能更好地反映预测值误差的实际情况.

The higher the norm index, the more it focuses on large values and neglects small ones. This is why the RMSE is more sensitive to outliers than the MAE. But when outliers are exponentially rare (like in a bell-shaped curve), the RMSE performs very well and is generally preferred.