Microsoft
DP-100 · Question #162
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 lea
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Train and deploy models
Question
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?
Options
- Anormalized_mean_absolute_error
- BAUC_weighted
- Caccuracy
- Dnormalized_root_mean_squared_error
- Espearman_correlation
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Topics
#Classification Metrics#Imbalanced Data#Model Evaluation#Automated Machine Learning