Median absolute error is a robust measure of the accuracy of a model's predictions, calculated as the median of the absolute differences between predicted values and actual values. This metric helps to evaluate the performance of predictive models by providing a summary statistic that is less sensitive to outliers compared to other error metrics, like mean absolute error. By focusing on the median, it gives a better indication of central tendency in error distribution, making it particularly useful in loss functions for optimizing model performance.
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