You're working on a supervised learning problem and need to decide which input features to create, transform, or exclude before training a model.
How would you discuss feature engineering choices for a predictive model?
Choosing useful raw and derived featuresHandling categorical variables, skew, and missingnessAvoiding leakage in time-based predictionUsing cross-validation and regularization to validate feature value