feature-engineering
Engineer ML features safely and effectively — feature types, encoding (one-hot/target/ordinal), scaling, missing-value and outlier handling, feature selection, pipelines and feature stores, and above all avoiding target leakage and train/serve skew. Deep reference with runnable leakage checks.
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Overview
Features decide whether a model works. A mediocre algorithm on great features beats a great algorithm on mediocre features almost every time. This skill is the deep reference for turning raw data into model inputs without lying to yourself — because the most common way feature engineering fails is silent: an offline metric that looks brilliant and collapses in production. Heavy detail lives in ref
What it covers
- Feature types and what they need
- Encoding and scaling
- Missing values and outliers
- Target leakage and train/serve skew — the big failure mode
- Feature selection
- Pipelines and feature stores