What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
For evaluating a regression model, the correct metrics are:
R² (Coefficient of Determination)
RMSE (Root Mean Squared Error)
Explanation: RMSE measures the average magnitude of the errors between predicted and actual values. It is a common metric for evaluating regression models.
Explanation: Balanced accuracy is a classification metric used to handle imbalanced datasets. It calculates the average of recall obtained on each class.
Relevance: Not applicable to regression.
Conclusion:
For evaluating a regression model, the correct metrics are:
R² (Coefficient of Determination)
RMSE (Root Mean Squared Error)
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