I started with resampling (SMOTE, random oversampling, undersampling the majority) and class weighting in the loss function, which felt like the expected answer.
Start by clarifying the problem context—dataset size, imbalance ratio, and business metric—then discuss a combination of data-level, algorithm-level, and evaluation strategies. Emphasize that the choice depends on trade-offs between precision and recall, and that you would validate with appropriate metrics like PR-AUC or F1.
Pro tip: Mention that in production, you often care more about ranking (e.g., PR-AUC) than thresholded metrics, and that techniques like class weighting are usually preferred over naive oversampling due to computational efficiency and less overfitting risk.
Ask about the imbalance ratio, dataset size, and the business cost of false positives vs. false negatives. This determines whether to prioritize recall, precision, or a balance.
Avoid accuracy; use precision-recall AUC, F1, or Matthews correlation coefficient. For ranking tasks, PR-AUC is often more informative than ROC-AUC.
Consider oversampling (SMOTE, ADASYN) or undersampling, but be aware of overfitting and information loss. Use them as part of a pipeline with cross-validation.
Use class weights, focal loss, or cost-sensitive learning. Tree-based ensembles like XGBoost and LightGBM handle imbalance well with scale_pos_weight.
Use stratified cross-validation, monitor performance on a holdout set, and consider threshold tuning to optimize the business metric.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.