Feature Engineering

Feature engineering is the construction of the variables a model sees: aggregating a raw stream into windows, deriving ratios and rates of change, encoding categories, and building the summaries that make a pattern learnable. Even where deep learning has absorbed some of this work, the choice of what to compute and over what horizon remains where domain knowledge enters a model, and it usually moves performance more than the choice of architecture.

The recurring failure is temporal. A feature computed from data that would not have existed at prediction time - a value later revised, an aggregate that spans the moment being predicted - produces a model that scores beautifully in research and fails live. Avoiding it requires point-in-time inputs and features defined against event time, which is the same discipline that keeps a backtest honest.