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Answer: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (R²), and Mean Absolute Percentage Error (MAPE).
AutoML can use several evaluation metrics for regression problems, including Mean Absolute Error (MAE) which measures the average absolute difference between predicted and actual values, Root Mean Squared Error (RMSE) which penalizes larger errors more heavily, R-squared (R²) which indicates the proportion of variance explained by the model, and Mean Absolute Percentage Error (MAPE) which provides a relative measure of error. Each metric is suitable for different scenarios; for example, MAE is preferred when the dataset contains outliers, while RMSE is useful for comparing the performance of different models.
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Discuss the evaluation metrics that AutoML can use for regression problems. Explain how each metric quantifies the performance of a regression model and which scenarios each metric is most suitable for.
A
Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (R²), and Mean Percentage Error (MPE).
B
Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Adjusted R-squared, and Median Absolute Deviation (MAD).
C
Root Mean Squared Logarithmic Error (RMSLE), Coefficient of Determination (R²), Mean Bias Deviation (MBD), and Mean Absolute Scaled Error (MASE).
D
Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (R²), and Mean Absolute Percentage Error (MAPE).
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