# Feature Engineering
Status: public
Confidence: medium (0.715) (verified)
Last verified: 2026-05-28
Generation: ai_structured

## TL;DR
Feature engineering prepares model inputs through transformations, selection, and column-specific preprocessing. The quality risk is leakage: features must be useful without encoding information unavailable at prediction time.

## Core Explanation
Common steps include scaling numeric values, encoding categorical variables, selecting useful predictors, and applying different transformations to different data types.

## Detailed Analysis
This repair anchors the topic to scikit-learn documentation and avoids unsupported claims about feature engineering guaranteeing better models.

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