Features can be raw fields, engineered aggregates, tokens, pixels, or representations learned by another model. Their availability and computation must be consistent between training and real inference.
A feature is a measurable or derived input value used by a machine learning model.
Features can be raw fields, engineered aggregates, tokens, pixels, or representations learned by another model. Their availability and computation must be consistent between training and real inference.