How do you handle class imbalance in a classification problem?
Interviewer Expectations for this Question
01
Core Competency
Assesses fundamental understanding of Machine Learning conventions, runtime behavior, and memory/performance considerations.
02
Evaluation Criteria
Hiring managers look for precision, avoidance of ambiguous jargon, and ability to explain trade-offs under real production conditions.
Comprehensive Model Answer
Verified Solution
Class imbalance means one class is far rarer, so accuracy becomes misleading and models favor the majority.
Strategies:
- Resampling: oversample the minority (SMOTE synthesizes examples) or undersample the majority. Do this only on training folds, never before splitting.
- Class weights: set class_weight="balanced" or scale the loss so minority errors cost more.
- Threshold tuning: move the decision threshold to optimize the metric you care about, such as recall or F1.
- Metric choice: use precision-recall AUC, F1 or cost-based measures instead of accuracy or ROC-AUC.
- Anomaly framing: treat the rare class with one-class or isolation methods.
- Collect more minority data if feasible.
model = LogisticRegression(class_weight="balanced")
Always evaluate on a stratified holdout or with stratified cross-validation so each fold keeps the class ratio.
Candidate Response Strategy & Interview Tips
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