Question 840 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. Which metric should be used to evaluate model performance during training to ensure the model is learning to detect the positive class?
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
✓
F1 score
Accuracy is misleading for imbalanced datasets because a model that predicts the majority class all the time can achieve 99% accuracy. F1 score balances precision and recall, making it suitable for imbalanced classification. Precision, recall, and AUC are also useful, but F1 is a common single metric for imbalanced binary classification. Option A: F1 score correctly balances precision and recall. Option B: Accuracy is not suitable. Option C: Precision alone ignores recall. Option D: Recall alone ignores precision.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
F1 score
Why this is correct
F1 score balances precision and recall, making it a good single metric for imbalanced binary classification. It captures both false positives and false negatives.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets because a model that always predicts the majority class can achieve 99% accuracy without detecting any positive instances.
- ✗
Precision
Why it's wrong here
Precision alone ignores recall, so a model with high precision but low recall may miss many positive cases, which is not suitable when detecting the positive class is important.
- ✗
Recall
Why it's wrong here
Recall alone ignores precision, so a model with high recall but low precision may produce many false positives, which is often not acceptable in practice.
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Last reviewed: Jun 20, 2026
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