MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model and wants to evaluate its performance using a metric that is robust to class imbalance. Which metric should be used?
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
The F1 score is the harmonic mean of precision and recall and is robust to class imbalance because it considers both false positives and false negatives. Accuracy can be misleading with imbalanced classes.
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, not classification.
- ✗
Area under the ROC curve (AUC)
Why it's wrong here
AUC is also robust but is not the only metric; F1 is more direct for imbalanced datasets.
- ✓
F1 score
Why this is correct
F1 score balances precision and recall and is robust to class imbalance.
- ✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model ignores the minority class.
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