Which primary metric should you use?

You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure Machine Learning by using automated machine learning.

The training dataset that you are using is highly unbalanced.

You need to evaluate the classification model.

Which primary metric should you use?
A . normalized_mean_absolute_error
B . [spearman_correlation
C . AU
D . weighted
E . accuracy
F . normalized_root_mean_squared_error

Answer: C

Explanation:

AUC_weighted is a Classification metric.

Note: AUC is the Area under the Receiver Operating Characteristic Curve. Weighted is the arithmetic mean of the score for each class, weighted by the number of true instances in each class.

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