Question 1,454 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on a highly imbalanced dataset (99% negative class, 1% positive class). The model currently achieves 99% accuracy but only identifies 0.5% of true positives. Which metric should the data scientist focus on to improve model performance?
⚠ Common exam trap
The trap here is that candidates see 99% accuracy and assume the model is performing well, failing to recognize that accuracy is a deceptive metric in imbalanced datasets, while recall directly measures the model's ability to find the rare 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
✓
Recall
Recall (sensitivity) measures the proportion of actual positives correctly identified, which is critical when the dataset is highly imbalanced (99% negative, 1% positive) and the model fails to detect most positives (only 0.5% true positives). Improving recall directly addresses the model's inability to capture the minority class, even if it reduces precision or accuracy. In binary classification with severe class imbalance, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class, as seen here.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precision
Why it's wrong here
Precision focuses on false positives, not false negatives; recall is more important here.
- ✗
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is for regression, not classification.
- ✓
Recall
Why this is correct
Recall measures the ability to find all positive samples, which is crucial for imbalanced data.
- ✗
Accuracy
Why it's wrong here
Accuracy is not suitable for imbalanced datasets as it can be high even if the model fails to predict positives.
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Last reviewed: Jun 24, 2026
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