feature engineering

Tabular ML is won on features, and this question checks whether you handle the practical traps: target leakage in encodings, missingness that is informative, and fitting transforms on the wrong data. The signal is leakage-safe preprocessing. Here is the toolkit.

The textbook reflex ('drop one') is usually the wrong instinct, and whether collinearity matters at all hinges on your model and what you need from it. This is the answer that separates rote from understanding.

The single most common way SQL leaks the future into a training set is a careless join to a feature table. Point-in-time correctness is the fix, and it is an as-of join. Here is how to write it.

Bucketing turns a skewed continuous feature into something a model (or a report) handles well, and the choice between equal-width and equal-count bins changes everything on skewed data. Here is both in SQL.

Ads ranking is where calibrated probabilities meet hard latency and money. The signal is knowing that CTR must be calibrated (not just ranked), the feature and serving design, and the auction context. Here is the design that goes beyond 'train a classifier.'