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What is MAE?
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Answer from AI
MAE (Mean Absolute Error) is the average absolute error, a metric used to evaluate the quality of regression models in machine learning. It is calculated as the arithmetic mean of the absolute differences between predicted values and true values:
[ MAE = \frac{1}{n} \sum_{i=1}^n |y_i - \hat{y}_i| ]
where:
- (y_i) — true value,
- (\hat{y}_i) — predicted value,
- (n) — number of observations.
MAE shows the average magnitude of errors without considering their direction, making it understandable and interpretable. Unlike MSE (Mean Squared Error), MAE is less sensitive to outliers.
It is used to evaluate model performance and optimize parameters when the average error in the same units as the original data is important.