Deep Feature Synthesis: Automated Feature Engineering

Before any model-selection or hyperparameter search runs, an AutoML pipeline has to turn raw relational tables into a flat feature matrix — and it does this automatically with Deep Feature Synthesis. This simulator builds a small relational schema (Customers → Transactions → Categories), lets automated aggregation primitives (COUNT, SUM, MEAN, MAX, STD) walk the relationship graph at one or two hops of depth, and scores every synthesized feature against a hidden target with a live Pearson correlation. Watch the 3D scene light up each customer hub in proportion to how predictive its best auto-generated feature turned out to be, tune how much real signal exists versus noise, and see automated feature engineering either converge on the useful aggregate or, when the signal is weak, find nothing better than chance.