Huber Loss
Tags: #machine learningEquation
$$L_{\delta}(y,f(x)) = \left \{ \begin{aligned} & \frac{1}{2}(y-f(x))^{2}, \text{for} |y-f(x)| \le \delta \cr & \delta \times (|y-f(x)| - \frac{1}{2}\delta) \cr \end{aligned} \right. $$Latex Code
L_{\delta}(y,f(x)) = \left \{ \begin{aligned} & \frac{1}{2}(y-f(x))^{2}, \text{for} |y-f(x)| \le \delta \cr & \delta \times (|y-f(x)| - \frac{1}{2}\delta) \cr \end{aligned} \right.
Have Fun
Let's Vote for the Most Difficult Equation!
Introduction
Huber Loss is widely used in regression as compared to MSE loss. When the error term |y-f(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.
Discussion
Comment to Make Wishes Come True
Leave your wishes (e.g. Passing Exams) in the comments and earn as many upvotes as possible to make your wishes come true
-
Gerald MorganI'm determined to get a pass on this test.Beverly Cooper reply to Gerald MorganNice~2024-03-19 00:00:00.0 -
Lauren SanchezIf only I could pass this exam with flying colors.Julie Wright reply to Lauren SanchezGooood Luck, Man!2023-09-22 00:00:00.0 -
Rita RichmondThe tension is high, but I'm hopeful I'll pass this exam.Florence Stone reply to Rita RichmondGooood Luck, Man!2024-02-21 00:00:00.0
Reply