Absolute error loss is a loss function that quantifies the difference between the predicted value and the actual value, using the absolute value of this difference. This loss function is particularly useful in situations where you want to minimize the magnitude of the prediction errors without considering their direction, making it a straightforward measure of accuracy. It connects to the concepts of risk and Bayes risk by offering a way to evaluate and compare predictive models based on how well they minimize expected losses.
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