Loss Functions

Here, we denote the target random variable $y$

Mean Squared Error

\[L_{MSE}(\hat{y}) = \frac{1}{2}\int_{\mathbb{R}} (y-\hat{y})^2 p(y) dy\]

The value \(\hat{y}\) that minimizes MSE is the expected value of \(y\):

\[\begin{align*} 0 &= \partial_{\hat{y}} MSE(\hat{y})\\ &= \frac{1}{2}\int_{\mathbb{R} 2(y-\hat{y})(-1) p(y) dy\\ &= \int_{\mathbb{R} -y p(y) dy + \hat{y} \int_{\mathbb{R} p(y) dy\\ \hat{y} &= \mathbb{E}_y[y] \end{align*}\]

Mean Absolute Error

\[L_{MAE}(\hat{y}) = \int_{\mathbb{R}} |y - \hat{y}| p(y) dy\]

Quantile Regression Loss

Expectile Regression Loss