Feature Engineering
Turning raw columns into inputs a model can use, without building something that breaks in production.
5 articles, newest first.
- Mutual information for feature selection, and where it misleads It catches relationships correlation misses, including ones you do not want. What it measures, why estimating it is hard, and why a high score can mean a leak.
- Seasonality, holidays and the calendar features that earn their place Most time series move on a calendar, not a trend. Which calendar features actually help, why one-hot day-of-week is usually wrong, and the holidays that break every model.
- Encoding categorical variables without shooting yourself One-hot, ordinal, target encoding, and the high-cardinality problem — which encoding to use when, and the leakage trap inside target encoding.
- Missing data is information, not an inconvenience Why dropna() is a modelling decision in disguise, the three mechanisms of missingness, and imputation patterns that do not bury the signal.
- Feature engineering that survives contact with production A feature that boosts your offline metrics but cannot be computed at prediction time is worse than useless. How to build features that keep working after deployment.