huber loss
Huber loss is a loss function used in regression models that combines the best qualities of the mean absolute error and mean squared error by smoothly transitioning between them. It calculates the absolute difference between the predicted and actual values, but when the difference exceeds a certain threshold, it switches to computing the squared difference. This results in a robust loss function that is less sensitive to outliers compared to mean squared error.
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