NAVIGATION
Definition

Mean Absolute Error

Mean Absolute Error (MAE) is a mathematical loss metric used in regression models that calculates the average absolute differences between predicted values and actual target values.

Frequently Asked Questions

How does MAE differ from Mean Squared Error (MSE)?

MSE squares the errors (penalizing large outliers heavily). MAE calculates simple absolute differences, making it more robust to outlier noise.

What is the formula for MAE?

`MAE = (1/n) * sum(|y_pred - y_true|)`.

Quick Facts

  • CategoryMathematical Foundations
  • Key ApplicationRegression model validation, loss calculations, and prediction scoring.

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