Learning objectives
- • Spot target leakage, temporal leakage, and data contamination
- • Choose encoding, scaling, and missing-value strategies pragmatically
- • Align offline feature logic with production feature generation
Traditional ML
Feature Engineering and Leakage
Build disciplined intuition around feature creation, train-serving parity, and leakage detection.
Recommended on day 1995 minutesIntermediate
Interview prompts
- • How would you detect leakage in a churn model?
- • When should you avoid one-hot encoding?
Prerequisites