Which metric can you use to evaluate a classification model?

The correct answer is A. true positive rate. What does a good model look like? An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would…

Submitted by yasin.bd· Mar 30, 2026Describe fundamental principles of machine learning on Azure

Question

Which metric can you use to evaluate a classification model?

Options

Atrue positive rate
Bmean absolute error (MAE)
Ccoefficient of determination (R2)
Droot mean squared error (RMSE)

How the community answered

(52 responses)

A
94% (49)
B
2% (1)
D
4% (2)

Explanation

What does a good model look like? An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line. https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated- ml#classification

Topics

#Classification models#Evaluation metrics#True positive rate

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