scikit-base
Base classes for sklearn-like parametric objects
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports 3.10, 3.11, 3.12, 3.13, 3.14).
- Low friction installation with no runtime dependencies.
- Active maintenance with a recent release (12 days old) and ongoing repository activity.
License · maintenance · safety
permissive license (permissive) — BSD 3-Clause License permits commercial and private use with minimal restrictions; you must retain copyright notices and the license text in distributions.
last release 2026-08-02 (12 days) · last repo commit 2026-08-14 · 41 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,591,880 downloads/mo, #2,979 on PyPI
Alternatives
Verify before relying
pip install scikit-base
import scikit_base- 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.
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 on this page. Verify before relying on it.
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
scikit-base on PyPI
Before you install
Low friction installation with no runtime dependencies. Active maintenance with a recent release (12 days old) and ongoing repository activity.
Requires Python 3.10 or later (supports 3.10, 3.11, 3.12, 3.13, 3.14).
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_baseVerify 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 | permissive license permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 12 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,591,880 / month, #2,979 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: scikit_base-1.1.0-py3-none-any.whl
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See also sktime · skforecast · neuralforecast · pycaret · kfactory · cf-units · feature-engine · scikit-learn-extra · pytorch-forecasting · hmmlearn