Features that Matter: Engineering Better Predictions

Great models begin long before training starts. Features that Matter is a machine learning book devoted to the art and science of feature engineering—the process that most directly determines prediction quality, fairness, and robustness.
The writing traces how raw data becomes meaningful representation: selecting signals, transforming variables, encoding context, and preserving domain knowledge. Rather than treating features as preprocessing trivia, the book shows how they define what a model can and cannot learn.
Instead of formula-driven shortcuts, the focus is intentional design. Readers learn why better features often outperform more complex algorithms, how leakage sneaks in, and how feature choices influence bias, stability, and interpretability. Each chapter connects engineering decisions to downstream outcomes.
The tone is practical and insight-driven, designed for practitioners who want consistent gains without unnecessary complexity. Language remains clear and structured, blending intuition with repeatable techniques.
Features that Matter moves through exploratory analysis, feature creation, selection, validation, and maintenance—demonstrating how disciplined engineering leads to reliable predictions.
Key themes explored include:
• Feature engineering fundamentals
• Signal selection and transformation
• Bias and leakage prevention
• Interpretability and robustness
• Prediction quality over complexity
Features that Matter is for builders who want results—offering a blueprint for engineering predictions that actually hold up.