MLS-C01 Modeling Practice Question
A data scientist is training a binary classifier on an imbalanced dataset (95% negative, 5% positive). The model achieves 99% accuracy but only correctly identifies 2% of the positive samples. Which metric should the data scientist focus on to improve the model's performance?
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). Recall measures the proportion of actual positive samples correctly identified, which is critical for an imbalanced dataset where the model fails to detect positives. Option A (Precision) is not the primary focus because it measures the accuracy of positive predictions, not the ability to find all positives. Option B (RMSE) is a regression metric, not suitable for binary classification. Option D (Accuracy) is misleading because a model can achieve high 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 does not directly address the low identification of positives.
- ✗
RMSE
Why it's wrong here
RMSE is used for regression, not classification.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading when classes are imbalanced.
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