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Is Huber loss continuous?
For me, pseudo huber loss allows you to control the smoothness and therefore you can specifically decide how much you penalise outliers by, whereas huber loss is either MSE or MAE. Also, the huber loss does not have a continuous second derivative.
What is Huber norm?
The Huber norm is an hybrid error measure that is robust to outliers. Because it is differentiable everywhere, the Huber norm can be minimized with a gradient-based algorithm.
How do you interpret a log loss?
For any given problem, a lower log loss value means better predictions. Mathematical interpretation: Log Loss is the negative average of the log of corrected predicted probabilities for each instance.
When do you use the Huber loss function?
In statistics, the Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss. A variant for classification is also sometimes used. The Huber loss function describes the penalty incurred by an estimation procedure f. Huber (1964) defines the loss function piecewise by .
Which is more robust Huber loss or MSE?
Huber loss is more robust to outliers than MSE. It is used in Robust Regression, M-estimation and Additive Modelling. A variant of Huber Loss is also used in classification. The name is pretty self-explanatory. Binary Classification refers to assigning an object into one of two classes.
Which is less sensitive Huber loss or squared error loss?
Huber loss is less sensitive to outliers in data than the squared error loss. It’s also differentiable at 0. It’s basically absolute error, which becomes quadratic when error is small. How small that error has to be to make it quadratic depends on a hyperparameter, 𝛿 (delta), which can be tuned.
Which is better to train Mae or Huber loss?
Huber loss can be really helpful in such cases, as it curves around the minima which decreases the gradient. And it’s more robust to outliers than MSE. Therefore, it combines good properties from both MSE and MAE. However, the problem with Huber loss is that we might need to train hyperparameter delta which is an iterative process.