MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with a target variable that is highly imbalanced (only 1% positive class). The goal is to build a binary classifier. During exploratory data analysis, which metric is MOST appropriate to evaluate the performance of different sampling strategies before model training?
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 Receiver Operating Characteristic Curve (AUC ROC)
The most appropriate metric during exploratory data analysis for evaluating sampling strategies with imbalanced data is AUC ROC, as it is independent of the class distribution and measures the model's ability to distinguish between positive and negative classes regardless of the threshold. Option A (RMSE) is used for regression tasks, not classification. Option C (F1 score) depends on a specific threshold and can be affected by sampling changes. Option D (Accuracy) is misleading for imbalanced datasets because a high accuracy can be achieved by predicting the majority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is used for regression, not classification.
- ✓
Area Under the Receiver Operating Characteristic Curve (AUC ROC)
Why this is correct
AUC ROC is threshold-independent and robust to class imbalance.
- ✗
F1 score
Why it's wrong here
F1 score is threshold-dependent and may not reflect overall separability.
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Accuracy
Why it's wrong here
Accuracy is misleading because a classifier that predicts all negative would achieve 99% accuracy.
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