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.

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

View Answer

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