Interpret AUC Values and Handle Class Imbalance Techniques
Overview
This question evaluates understanding of the Area Under the ROC Curve (AUC) and its relationship to true positive rate and false positive rate, along with competency in handling severe class imbalance via approaches such as resampling, threshold tuning, and metric selection, testing both conceptual understanding of evaluation metrics and practical application of mitigation strategies. It is commonly asked in the Machine Learning domain to assess how well an interviewee can interpret classifier discrimination under skewed class distributions and choose appropriate evaluation and adjustment approaches during model development.
AUC and Class Imbalance in Binary Classification
Context
You are evaluating a binary classifier using ROC–AUC and need to reason about performance under severe class imbalance.
Tasks
- Define what the Area Under the ROC Curve (AUC) measures and how it relates to the True Positive Rate (TPR) and False Positive Rate (FPR).
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Interpret models with:
- AUC = 0.5
- AUC = 0.9
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List and briefly describe three techniques to handle severe class imbalance in binary classification, covering:
- Resampling
- Threshold tuning
- Metric selection
Solution
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