Feature Engineering
4concepts
31flashcards
28minutes of reading
- 01 Categorical Encoding Why one-hot encoding breaks down at high cardinality, how target encoding trades that for a leakage risk it must then defend against, and what each choice assumes about unseen categories.
- 02 Missing Data Mechanisms Whether imputation is safe depends on why the value is absent, and the three-way distinction between MCAR, MAR and MNAR decides which methods are valid and which quietly bias the result.
- 03 Target Leakage The failure mode where a feature encodes information unavailable at prediction time, why cross-validation cannot detect it, and the three structural forms it takes.
- 04 Train-Serve Skew The gap between how a feature is computed in a training pipeline and how it is computed in a serving path, which silently degrades a model that was never wrong in offline evaluation.