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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.
A. coefficient of determination (R2)
B. F1 score
C. root mean squared error (RMSE)
D. area under curve (AUC)
E. balanced accuracy
Correct Answer: AC
Explanation/Reference:
Explanation:
A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.
C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.
Incorrect Answers:
B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model.
D: aucROC or area under the curve (AUC) is used to evaluate a classification model.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics
