MLS-C01 Modeling Practice Question
A team is deploying a model for fraud detection. The dataset is highly imbalanced (99% legitimate, 1% fraudulent). They trained a logistic regression model and achieved 99% accuracy on the test set. However, the model fails to detect most fraud cases. Which metric should the team focus on to evaluate the model?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recall
For imbalanced datasets, accuracy is misleading because it can be high even if the model misses all fraud cases. Recall (true positive rate) measures the proportion of actual fraud cases correctly identified. Option A (Mean squared error) is for regression tasks, not classification. Option B (Precision) measures the proportion of predicted fraud cases that are actually fraud, but it may be high even if recall is low. Option D (Accuracy) is high (99%) but does not reflect the model's poor performance on the minority class. Therefore, recall is the most appropriate metric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Mean squared error
Why it's wrong here
MSE is for regression, not classification.
- ✗
Precision
Why it's wrong here
Precision could be high if the model only flags obvious fraud, missing many.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual fraud cases correctly identified.
- ✗
Accuracy
Why it's wrong here
Accuracy is high due to class imbalance but does not reflect fraud detection.
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