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27/08/2022 1:36 am
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
Suggested Answer: AC
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
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