feature engineering

On enterprise data, the feature pipeline routinely buys 2-5x the lift of a model swap. Here's the worked churn example, and the leakage rule every engineered feature must pass, that makes the argument stick.

The honest version: deep learning learns its own features but is hungry for data and compute, and on a customer's 50,000-row tabular dataset a gradient-boosted tree wins on accuracy, cost, and time-to-ship. The dividing line and the conditions that flip it.

Profile before you model: shape, types, target balance, leakage check. Then the part most candidates botch, that a missing value is often the most predictive feature in the table, so you flag it before you fill it. The first-hour playbook and the imputation decisions that survive production.