---
id: scikit-learn-extra
version: "0.3.0"
license: new BSD
license_treatment: permissive
maintenance: active
---

# scikit-learn-extra — A set of tools for scikit-learn.

License: permissive · Maintenance: active · Downloads: 281.4K/mo

## What it is and what it does

scikit-learn-extra is a Python extension module that adds machine learning algorithms to scikit-learn that are useful but fall outside scikit-learn's strict inclusion criteria—typically due to novelty or lower citation counts. It maintains API compatibility with scikit-learn, so algorithms work with the same fit/predict interface developers already know.

The package depends on numpy, scipy, and scikit-learn itself, making it a natural fit for projects already using the scikit-learn ecosystem. It provides pre-built wheels for multiple Python versions and platforms (macOS, Linux, Windows), though installation requires compilation support on some systems. The repository is actively maintained and has no known security vulnerabilities.

Use it for:

- Access clustering algorithms when scikit-learn's built-in options don't fit your problem.
- Experiment with newer machine learning methods in production code while maintaining scikit-learn compatibility.
- Extend scikit-learn pipelines with additional estimators that follow the same interface conventions.
- Use algorithms from recent research papers that haven't yet reached scikit-learn's maturity threshold.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Extends scikit-learn with additional machine learning algorithms that don't meet scikit-learn's inclusion criteria, such as newer or less-cited methods.

Yes, if you need specific algorithms beyond scikit-learn's core set and are comfortable with a smaller, less-mature project. The permissive BSD license, active maintenance, and scikit-learn-compatible API make it low-risk. However, the latest release was over a year ago—verify that the algorithms you need are stable and well-documented before adopting for critical production work.

## Install

pip install scikit-learn-extra
uv add scikit-learn-extra
poetry add scikit-learn-extra

## Installing scikit-learn-extra

Before you install: Medium install friction due to compiled wheel dependencies across multiple Python versions and platforms. Repository is actively maintained with recent commits, though the latest release was over a year ago.

License in practice: Released under new BSD, a permissive license that allows commercial and private use with minimal restrictions—suitable for most projects.

Quickstart:

pip install scikit-learn-extra

from scikit_learn_extra.cluster import KMedoids
import numpy as np

model = KMedoids(n_clusters=3)
model.fit(X)

Requires Python >=3.6 and scikit-learn >=0.24 with its dependencies (numpy, scipy).

Verify before relying:

- Which specific algorithms are included and how they differ from scikit-learn's offerings.
- Performance characteristics and computational overhead compared to standard scikit-learn.
- Active user community size and support availability beyond the repository.

## Package facts

- License: new BSD (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 281.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

scikit-learn extensions, additional machine learning algorithms, scikit-learn contrib algorithms, experimental ml methods, scikit-learn compatible tools, machine learning algorithm library, sklearn extra estimators, scikit-learn-extension, machine-learning-algorithms

[View on SkillFed](https://skillfed.io/packages/scikit-learn-extra) · [View on PyPI](https://pypi.org/project/scikit-learn-extra/)