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

**Answer: **A,C

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.

Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics

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