DP-100 Question #45: Real Exam Question with Answer & Explanation

The correct answer is B: AUC Curve. One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the classifier's performance is (that is m

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Question

You are creating a binary classification by using a two-class logistic regression model. You need to evaluate the model results for imbalance. Which evaluation metric should you use?

Options

ARelative Absolute Error
BAUC Curve
CMean Absolute Error
DRelative Squared Error

Explanation

One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model- performance#evaluating-a-binary-classification-model

Topics

#Model Evaluation#Binary Classification#Class Imbalance#AUC

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