Feature engineering is crafting the input variables models learn from: transforming raw data into signals (ratios, aggregates, time windows, encodings) that mak
Crafting the input variables models learn from, transforming raw data into signals like ratios, aggregates, time windows, and encodings that make patterns learnable. In classic ML it is often where most of the predictive power comes from.
Yes for tabular business ML. Deep learning automated feature extraction for images and text, but fraud, credit, and churn models still run on engineered features, and the discipline now extends to feature stores that serve them consistently in production.