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Question 9 - AI-900 discussion

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Which metric can you use to evaluate a classification model?

A.
true positive rate
Answers
A.
true positive rate
B.
mean absolute error (MAE)
Answers
B.
mean absolute error (MAE)
C.
coefficient of determination (R2)
Answers
C.
coefficient of determination (R2)
D.
root mean squared error (RMSE)
Answers
D.
root mean squared error (RMSE)
Suggested answer: A

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.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification

asked 26/09/2024
Tim Dekker
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