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27/09/2022 5:25 am
Which metric can you use to evaluate a classification model?
- A . true positive rate
- B . mean absolute error (MAE)
- C . coefficient of determination (R2)
- 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
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