---
id: scikit-base
version: "1.1.0"
license: BSD 3-Clause License Copyright (c) 2022, skbase Developers All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# scikit-base — Base classes for sklearn-like parametric objects

License: permissive · Maintenance: active · Downloads: 2.6M/mo

## What it is and what it does

scikit-base is a framework library that provides reusable base classes for building machine-learning-like packages following scikit-learn and sktime conventions. Rather than implementing algorithms, it standardizes the structure and behavior of parametric objects so that packages built on top of it inherit consistent interfaces and behavior. This reduces boilerplate and makes it easier for developers to create new packages that feel familiar to users of the scikit-learn ecosystem.

The package has no runtime dependencies, making it lightweight to add to your project. It targets developers building new ML frameworks or domain-specific packages who want to adopt scikit-learn-like design patterns without reimplementing the foundation. The active maintenance and recent release history suggest it is actively used and developed.

Use it for:

- Building a new machine-learning package that needs to follow scikit-learn conventions for parameter handling and API consistency.
- Creating domain-specific estimators that inherit standard parametric object behavior from a common base.
- Developing a framework extension or plugin system where all components should share a common interface.
- Standardizing internal utilities within a research project to ensure consistent object behavior across modules.

## Worth the install?

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

Provides base classes and design patterns for building scikit-learn-like and sktime-like parametric objects, making it easier to create packages that follow these established conventions.

Yes, if you are building a new ML package or framework and want to adopt scikit-learn-like conventions without implementing the foundation yourself. The zero runtime dependencies, permissive BSD 3-Clause License, and active maintenance make it a low-risk dependency. Not relevant for end-users of ML packages—primarily a tool for package developers and framework authors.

## Install

pip install scikit-base
uv add scikit-base
poetry add scikit-base

## Installing scikit-base

Before you install: Low friction installation with no runtime dependencies. Active maintenance with a recent release (12 days old) and ongoing repository activity.

License in practice: BSD 3-Clause License permits commercial and private use with minimal restrictions; you must retain copyright notices and the license text in distributions.

Quickstart:

pip install scikit-base

import scikit_base

Requires Python 3.10 or later (supports 3.10, 3.11, 3.12, 3.13, 3.14).

Verify before relying:

- What specific base classes and design patterns are provided for parametric objects.
- Whether the package includes utilities for parameter validation, serialization, or introspection.
- Performance or memory overhead of using these base classes in production systems.
- How to use the framework factory to build new packages following scikit-learn conventions.

## Package facts

- License: BSD 3-Clause License Copyright (c) 2022, skbase Developers All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 2.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

scikit-learn base classes, parametric object framework, sklearn-like design patterns, sktime base classes, machine learning framework factory, ml-framework, design-patterns, sklearn-compatible

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