Answer-first summary for fast verification
Answer: All of the above
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.
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What evaluation metrics are automatically calculated for each run in an AutoML experiment when addressing regression problems?
A
Root Mean Square Error (RMSE)
B
Mean Absolute Error (MAE)
C
Coefficient of Determination (R-squared)
D
Mean Square Error (MSE)
E
All of the above