How can I handle feature engineering for imbalanced datasets?

How can I handle feature engineering for imbalanced datasets?

Feature engineering for imbalanced datasets involves techniques like resampling, creating synthetic samples, and selecting relevant features.

What are some techniques for resampling?

Common resampling techniques include oversampling the minority class, undersampling the majority class, and using SMOTE (Synthetic Minority Over-sampling Technique).

Can you explain SMOTE in more detail?

SMOTE generates synthetic samples by interpolating between existing minority class samples, helping to balance the dataset.

Are there any specific feature selection methods I should consider?

Yes, methods like Recursive Feature Elimination (RFE), LASSO, and tree-based feature importance can be effective.