---
atomic_facts:
  - id: fact-ai-001
    statement: >-
      scikit-learn describes preprocessing as transforming raw feature vectors into representations
      more suitable for downstream estimators, including standardization and scaling utilities.
    source_title: Preprocessing data
    source_url: https://scikit-learn.org/stable/modules/preprocessing.html
    confidence: medium
  - id: fact-ai-002
    statement: >-
      scikit-learn documents missing-value imputation as replacing missing values with constants or
      statistics such as the mean, median, or most frequent value.
    source_title: Imputation of missing values
    source_url: https://scikit-learn.org/stable/modules/impute.html
    confidence: medium
  - id: fact-ai-003
    statement: >-
      scikit-learn warns that preprocessing transformations should be learned from training data,
      not from the test set, to avoid data leakage.
    source_title: Common pitfalls and recommended practices
    source_url: https://scikit-learn.org/stable/common_pitfalls.html
    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-2026-00281
is_live_document: false
known_gaps:
  - >-
    This field is under active research and rapid development; some conclusions may evolve with new
    evidence or technological advances
  - >-
    Certain sub-topics are covered at a general level; specialized edge cases and nuanced
    applications may not be fully addressed
language: en
last_verified: "2026-05-28"
primary_sources:
  - id: ps-data-preprocessing-1
    title: Preprocessing data
    type: documentation
    year: 2026
    institution: scikit-learn
    url: https://scikit-learn.org/stable/modules/preprocessing.html
  - id: ps-data-preprocessing-2
    title: Imputation of missing values
    type: documentation
    year: 2026
    institution: scikit-learn
    url: https://scikit-learn.org/stable/modules/impute.html
  - id: ps-data-preprocessing-3
    title: Common pitfalls and recommended practices
    type: documentation
    year: 2026
    institution: scikit-learn
    url: https://scikit-learn.org/stable/common_pitfalls.html
schema_type: TechArticle
secondary_sources: []
title: Data Preprocessing
updated: "2026-05-28"
---
## TL;DR
Data preprocessing turns raw data into inputs that machine-learning estimators can use more reliably. The key public claims are scaling, imputation, and leakage prevention.

## Core Explanation
Common preprocessing steps include feature scaling, missing-value imputation, encoding, and train/test separation. The safest rule is to fit preprocessing steps on training data only, then apply the learned transformation to validation or test data.

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