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. The model achieves 99% accuracy but only identifies 5% of the actual positives. Which metric should the data scientist use to evaluate model performance?
⚠ Common exam trap
The MLS-C01 exam often tests the trap that high accuracy implies good performance on imbalanced datasets, leading candidates to choose accuracy without considering class distribution or the specific failure mode (low recall).
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 by the model. With only 5% of positives detected, recall is 0.05, which directly reveals the model's failure to capture the minority class despite high accuracy. In imbalanced datasets, accuracy is misleading because the model can achieve 99% accuracy by simply predicting the majority class (negative) for all instances.
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 problems.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified.
- ✗
Precision
Why it's wrong here
Precision measures the proportion of predicted positives that are actual positives.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.