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Feature Engineering Q&A
1What is feature engineering?
Answer: Creating useful model inputs from raw data.
2Why is it important?
Answer: Better features often outperform complex algorithms.
3Examples of transformations?
Answer: Log transform, binning, interaction terms, ratios.
4How encode categories?
Answer: One-hot, ordinal, target, frequency encodings.
5When use one-hot?
Answer: Low-cardinality nominal categories.
6What is leakage in FE?
Answer: Feature uses future/target info unavailable at prediction time.
7How create time features?
Answer: Extract day/week/month/hour and lag/rolling statistics.
8Scaling needed when?
Answer: For distance/gradient-based models (KNN, SVM, LR).
9How select useful features?
Answer: Domain logic, importance metrics, correlation checks, CV performance.
10What are interaction features?
Answer: Combined variables capturing joint effects.
11How document FE pipeline?
Answer: Versioned transformation steps and reproducible code.
12One-line summary?
Answer: Feature engineering converts domain insight into predictive power.