---
atomic_facts:
  - id: af-feature-engineering-1
    statement: Feature preprocessing can include scaling, centering, normalization, and encoding transformations.
    source_title: Preprocessing data
    source_url: https://scikit-learn.org/stable/modules/preprocessing.html
    confidence: medium
  - id: af-feature-engineering-2
    statement: Feature selection methods aim to remove irrelevant or redundant features before modeling.
    source_title: Feature selection
    source_url: https://scikit-learn.org/stable/modules/feature_selection.html
    confidence: medium
  - id: af-feature-engineering-3
    statement: ColumnTransformer applies different preprocessing pipelines to different columns in heterogeneous tabular data.
    source_title: ColumnTransformer for heterogeneous data
    source_url: https://scikit-learn.org/stable/modules/compose.html#column-transformer
    confidence: medium
category: ai
completeness: 0.88
confidence: medium
conflict_of_interest: none_declared
created_date: '2026-05-22'
data_period: static
derived_from_human_seed: true
disputed_statements: []
generation_method: ai_structured
id: kb-2026-00280
is_live_document: false
known_gaps:
  - Domain-specific leakage risks
  - Monitoring feature drift after deployment
language: en
last_verified: '2026-05-28'
primary_sources:
  - id: ps-feature-engineering-1
    title: Preprocessing data
    type: technical_documentation
    year: 2024
    institution: scikit-learn
    url: https://scikit-learn.org/stable/modules/preprocessing.html
  - id: ps-feature-engineering-2
    title: Feature selection
    type: technical_documentation
    year: 2024
    institution: scikit-learn
    url: https://scikit-learn.org/stable/modules/feature_selection.html
  - id: ps-feature-engineering-3
    title: ColumnTransformer for heterogeneous data
    type: technical_documentation
    year: 2024
    institution: scikit-learn
    url: https://scikit-learn.org/stable/modules/compose.html#column-transformer
schema_type: TechArticle
secondary_sources: []
title: Feature Engineering
updated: '2026-05-28'
---
## 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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