This tool computes the average absolute difference between predicted and actual values in a dataset. For example, if a model predicts house prices and the differences between the predictions and real prices are $5,000, $10,000, and $2,000, the average of these absolute differences represents the metric’s output. This provides a straightforward measure of prediction accuracy in easily understandable units.
Averaging the magnitude of errors offers a clear, interpretable metric for model performance evaluation. Unlike other metrics sensitive to outliers, this approach treats all errors equally, providing robustness in various applications like forecasting and regression analysis. Its historical roots lie in basic statistical methods, predating more complex evaluation techniques, and its simplicity continues to make it a valuable tool for quick assessments of predictive accuracy.