Learn how R2 and RMSE serve as vital measures in assessing the accuracy and prediction of regression models for better data-driven decisions.
Table of Contents
Question
What are two metrics that you can use to evaluate a regression model?
A. coefficient of determination (R2)
B. F1 score
C. root mean squared error (RMSE)
D. area under curve (AUC)
E. balanced accuracy
Answer
A. coefficient of determination (R2)
C. root mean squared error (RMSE)
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
Microsoft Learn > .NET > ML.NET guide > Resources > Evaluate your ML.NET model with metrics
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