MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with a binary target variable. The dataset is highly imbalanced (99% negative class). Which metric is most appropriate for evaluating the model's performance during exploratory data analysis?
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
✓
Area Under the ROC Curve (AUC-ROC)
In highly imbalanced datasets (99% negative class), accuracy is misleading because a model that predicts the majority class always achieves 99% accuracy. Precision focuses on false positives and is threshold-dependent. F1 score balances precision and recall but is sensitive to the chosen threshold and may not reflect overall performance. AUC-ROC evaluates the model's ability to distinguish between classes across all thresholds and is robust to class imbalance, making it the most appropriate metric for initial model evaluation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading because a model predicting all negatives would achieve 99% accuracy.
- ✗
Precision
Why it's wrong here
Precision alone does not capture recall trade-off.
- ✗
F1 Score
Why it's wrong here
F1 score is useful but depends on threshold; AUC-ROC is better for initial exploration.
- ✓
Area Under the ROC Curve (AUC-ROC)
Why this is correct
AUC-ROC is insensitive to class imbalance and provides a global measure of performance.
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