MLA-C01 ML Model Development Practice Question
A machine learning engineer is evaluating a binary classification model that predicts customer churn. The model achieves 95% accuracy, but the engineer suspects class imbalance is causing a misleading metric. Which THREE evaluation steps should the engineer perform to properly assess the model? (Choose THREE.)
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
✓
Calculate precision, recall, and F1-score
Accuracy is misleading for imbalanced datasets. Confusion matrix, precision/recall/F1, and AUC-ROC are robust metrics. RMSE is for regression. MAE is also for regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Calculate RMSE
Why it's wrong here
RMSE is a regression metric, not suitable for classification.
- ✓
Calculate precision, recall, and F1-score
Why this is correct
Precision, recall, and F1 are class-imbalance-aware metrics.
- ✗
Compute Mean Absolute Error (MAE)
Why it's wrong here
MAE is for regression, not classification.
- ✓
Plot the ROC curve and compute AUC
Why this is correct
ROC curve and AUC are threshold-independent and robust to imbalance.
- ✓
Compute the confusion matrix
Why this is correct
Confusion matrix shows true positives, false positives, etc., revealing performance per class.
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