Raw data rarely arrives in the shape a model learns best from — engineering transforms it into representations that actually separate the classes.
feature' = transform(feature)
keep if importance > selection_threshold
- Raw features — original untransformed input variables in the dataset.
- Transform connectivity — how many raw features feed into any given engineered feature (e.g. interaction terms).
- Transform strength — how aggressively scaling/encoding/binning reshapes each raw feature.
- Selection threshold — importance score an engineered feature needs to survive selection into the final model.
Kaggle-winning models routinely credit feature engineering, not model choice, as the single biggest driver of score improvement.