Data and feature engineering · MLD-0271
Data quality
Fitness of data for a purpose, including correctness, completeness, relevance, and consistency.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Audit missing labels and corrupted files.
Original caveat
Quality is task-dependent, not a single universal score.
Context and aliases
Level: Intermediate. Stages: Data · Training · Evaluation.
Tags: ML · DL · SLM · LLM.
Aliases: None recorded.
Related concepts
Concepts linking here
Technology connections
- Argilla — Human feedback and dataset-curation platform.
- Cleanlab — Data-centric tools for finding label and other dataset issues.
- CVAT — Computer-vision annotation tool.
- Docling — Document parsing and conversion toolkit.
- FiftyOne — Visual dataset exploration and evaluation application.
- Great Expectations — Data validation framework using declarative expectations.
- Label Studio — Configurable data-labeling application.
- Pandera — DataFrame schema and statistical validation library.
- TensorFlow Data Validation — Dataset statistics and anomaly-detection tooling.
- Unstructured — Document ingestion and partitioning toolkit.
Original references
- scikit-learn: cross-validation ↗ · Reference link
- scikit-learn: missing-value imputation ↗ · Reference link
- scikit-learn: preprocessing ↗ · Reference link
- scikit-learn: feature selection ↗ · Reference link
- Original reference URL ↗