Feature Engineering

Feature engineering converts raw data into signals that better represent the problem a model is trying to solve. It may include scaling values, combining fields, extracting time-based patterns, encoding categories, or creating domain-specific indicators that improve predictive performance. It is used when teams need models to learn patterns from data and generalize those patterns to new examples rather than relying only on hand-written rules.