# koaning/scikit-lego

Extra blocks for scikit-learn pipelines.

Repository: https://github.com/koaning/scikit-lego
Canonical: https://ross.abutalabs.com/products/scikit-lego
Homepage: https://koaning.github.io/scikit-lego/
Language: Python
License: MIT
License Family: permissive
Topics: scikit-learn, machine-learning, common-sense
Last push: 2026-08-09T07:11:21+00:00

## Health v2 (maintenance only)
Score: 87/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 68, longevity 100
- inputs: {"age_days": 2781, "days_push": 24, "days_rel": 59, "gap_med": 134, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1407, forks 130 (observed 2026-08-28T04:04:38.420482+00:00)

## What it is
scikit-lego is a Python library of extra building blocks—custom transformers, models, metrics, and datasets—that are fully compatible with scikit-learn pipelines. It consolidates commonly rewritten utilities into a tested, MIT-licensed package that adheres to scikit-learn's API standards.

## Use cases
- add custom transformers to scikit-learn pipelines
- find scikit-learn compatible models like GMM classifier
- load example datasets for teaching machine learning
- evaluate model fairness with scikit-learn metrics
- add noise augmentation inside a pipeline
- extend scikit-learn without writing boilerplate transformers

## When to choose
- you already use scikit-learn and need extra transformers, models, or metrics that fit the same API
- you want tested, reusable pipeline components instead of copy-pasting custom code
- you teach or demo machine learning and need small interesting datasets with fairness concerns

## When to avoid
- you need deep learning or GPU-based modeling outside the scikit-learn ecosystem
- you require a library formally affiliated with or maintained by the scikit-learn project
- you need a full AutoML or end-to-end ML platform rather than pipeline building blocks

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, testing
- domain: machine-learning, data-science
- platform: python
- tags: scikit-learn, pipelines, transformers, fairness, datasets

## Member repositories
- koaning/scikit-lego (main) score 87

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:38.420482+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:38:38.818578+00:00, confidence not recorded.
  - readme: https://github.com/koaning/scikit-lego (fetched 2026-08-28T04:04:38.420482+00:00, sha c0a378dba647)
  - homepage: https://koaning.github.io/scikit-lego/ (fetched 2026-08-29T11:52:22.155317+00:00, sha ca7b1c393e13)
  - registry_pypi: https://pypi.org/pypi/scikit-lego/json (fetched 2026-08-29T11:52:22.164364+00:00, sha 5a5decbac30c)
- Data as of 2026-08-30T08:39:29.467469+00:00.