MLS-C01 Modeling Practice Question
A data scientist is building a binary classifier and wants to evaluate model performance. Which THREE metrics are most commonly used?
⚠ Common exam trap
AWS often tests the distinction between regression and classification metrics, and the trap here is that candidates mistakenly apply regression metrics like MAE or RMSE to binary classification problems.
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
✓
Precision
Precision is a core metric for binary classifiers, measuring the proportion of true positive predictions among all positive predictions. It is especially important when the cost of false positives is high, such as in spam detection or fraud alert systems.
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 Absolute Error
Why it's wrong here
Regression metric.
- ✗
RMSE
Why it's wrong here
Regression metric.
- ✓
Precision
Why this is correct
Common classification metric.
- ✓
Recall
Why this is correct
Common classification metric.
- ✓
Accuracy
Why this is correct
Common classification metric.
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Senior Network & Security Engineer · founder of Courseiva
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