Mean Absolute Error (MAE): Definition, Range, and Interpretation in Machine Learning

MAE stands for Mean Absolute Error. It's a commonly used metric in regression analysis to measure the average distance between the predicted and actual values. MAE is calculated by taking the absolute difference between the predicted and actual values and then taking the average of those differences.

The range of MAE is from 0 to infinity. A lower MAE indicates a better fit between the predicted and actual values, while a higher MAE indicates a poorer fit. MAE is measured in the same units as the original data, making it easy to interpret and compare across different models.