Huber Loss
Tags: #machine learningEquation
$$L_{\delta}(y,f(x)) = \left \{ \begin{aligned} & \frac{1}{2}(yf(x))^{2}, \text{for} yf(x) \le \delta \cr & \delta \times (yf(x)  \frac{1}{2}\delta) \cr \end{aligned} \right. $$Latex Code
L_{\delta}(y,f(x)) = \left \{ \begin{aligned} & \frac{1}{2}(yf(x))^{2}, \text{for} yf(x) \le \delta \cr & \delta \times (yf(x)  \frac{1}{2}\delta) \cr \end{aligned} \right.
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Introduction
Huber Loss is widely used in regression as compared to MSE loss. When the error term yf(x) is less or equal than delta, the loss is quadratic the same as MSE Mean Squared Error loss. When the regression error term is larger or equal than delta, the loss is linear.
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