I started with the obvious stuff, resampling and class weights, but then they pushed on whether those were actually solving overfitting or just shifting the decision boundary.
Start by acknowledging that class imbalance is common and can lead to overfitting to the majority class. Then, structure your answer around three pillars: data-level techniques, algorithm-level techniques, and evaluation metrics. Emphasize that the choice depends on the specific problem and that you would validate using appropriate metrics like precision-recall AUC or F1-score.
Pro tip: Mention that you would first quantify the imbalance and its impact on business metrics, then choose techniques that align with the cost of false positives vs. false negatives. This shows you think beyond just model performance to business impact.
Quantify the class distribution and assess how it affects model performance. Consider the business context to determine the cost of different error types.
Apply resampling methods such as oversampling the minority class (e.g., SMOTE), undersampling the majority class, or a combination. Discuss the trade-offs, such as potential overfitting from oversampling or information loss from undersampling.
Use class weights, cost-sensitive learning, or ensemble methods like balanced bagging or boosting. Explain how these adjust the learning process to penalize misclassification of the minority class more heavily.
Choose metrics robust to imbalance, such as precision-recall AUC, F1-score, or Matthews correlation coefficient. Avoid accuracy, which can be misleading.
Use stratified cross-validation to ensure representative splits. Iterate on techniques and monitor both performance and overfitting via learning curves or hold-out sets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.