Which metric is used to evaluate the accuracy of regression predictions?

The metric used to evaluate the accuracy of regression predictions is Mean Absolute Error (MAE). This metric measures the average magnitude of the errors between predicted values and actual values, without considering their direction. It is calculated by taking the absolute difference between the predicted and actual values, summing those absolute differences, and then dividing by the number of observations.

MAE provides a clear understanding of the average error in the same units as the data, making it easy to interpret. When applying regression models, MAE is invaluable because it enables practitioners to assess how close their predictions are to the actual outcomes, allowing for adjustments and improvements in modeling approaches.

In contrast, metrics like F1 Score, Precision, and Recall are primarily used in classification tasks rather than regression. F1 Score is the harmonic mean of precision and recall, applying to scenarios where the goal is to balance the consideration of false positives and false negatives. Precision measures the proportion of true positive results in the context of predicted positives, while Recall assesses how many actual positives were correctly identified. Each of these metrics serves a purpose for classification problems, not regression analysis.