Microsoft Exam AI-900 Topic 1 Question 21 Discussion

Actual exam question for Microsoft's AI-900 exam

Question #: 21
Topic #: 1

[All AI-900 Questions]

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.

Acoefficient of determination (R2)

BF1 score

Croot mean squared error (RMSE)

Darea under curve (AUC)

Ebalanced accuracy

Show Suggested Answer

Suggested Answer: A, C

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.

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

by Shalon at May 03, 2022, 05:02 PM

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