Question 24
Content Domain 3: ModelingWhich of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
Correct answer: D
Explanation
Area Under the ROC Curve (AUC) is a standard metric for comparing classification models because it measures how well a model separates positive from negative classes across all thresholds. It summarizes the ROC curve into a single value, making it useful for evaluating and ranking models against each other.
Why each option is right or wrong
A. Recall.
Recall measures sensitivity at a specific threshold, so it is incomplete for overall model comparison.
B. Misclassification rate.
Misclassification rate depends on one chosen threshold and can hide ranking performance differences.
C. Mean absolute percentage error (MAPE).
MAPE is a regression error metric for numeric predictions, not classification outcomes.
D. Area Under the ROC Curve (AUC).
For binary classification, ROC analysis plots the true positive rate against the false positive rate at every decision threshold, and the area under that curve is a single threshold-independent summary of separability. AUC ranges from 0.0 to 1.0, with 0.5 indicating no better than random ranking and 1.0 indicating perfect discrimination, which makes it a standard way to compare competing classifiers on the same task.