What does the Mean Absolute Error (MAE) metric represent in regression analysis?
- Average of absolute errors
- Average of squared errors
- Sum of absolute errors
- Sum of squared errors
The Mean Absolute Error (MAE) represents the average of the absolute errors between the predicted values and the actual values. Unlike MSE, MAE does not square the errors, so it doesn't give extra weight to larger errors, making it more robust to outliers. It provides an understanding of how much the predictions deviate from the actual values on average.
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