2 docs tagged with "features"

Preprocessing decides more of a model's final performance than the choice of algorithm does. It also has to be treated as part of the model, not a one-off step: whatever transformation is applied to training data must travel with the model into production and be fitted only on training data, never on validation or test data (see Train/Validation/Test Splits).

The same feature, computed two different ways in two different places, gives two different answers — training pipeline code and serving pipeline code drift apart, quietly, until a model that scored well offline behaves differently in production for no reason anyone can immediately find.