In an AutoML experiment, especially for regression problems, multiple evaluation metrics are calculated to assess model performance. These include:

• Mean Absolute Error (MAE): Measures the average magnitude of errors in predictions, without considering direction.
• Coefficient of Determination (R-squared): Indicates the proportion of variance in the dependent variable predictable from independent variables.
• Root Mean Square Error (RMSE): The square root of the average squared differences between prediction and actual observation.
• Mean Square Error (MSE): Similar to RMSE, it measures the average of the squares of the errors.

AutoML frameworks compute these metrics to provide a comprehensive view of the model's accuracy, consistency, and predictive power.