---
id: sklearn-compat
version: "0.1.6"
license: unclear
license_treatment: permissive
maintenance: active
---

# sklearn-compat — Ease support for compatible scikit-learn estimators across versions

License: permissive · Maintenance: active · Downloads: 2.4M/mo

## What it is and what it does

sklearn-compat is a small utility package that centralizes backward-compatible wrappers for scikit-learn's private developer APIs. It solves a specific problem for maintainers of scikit-learn-compatible third-party libraries: each time scikit-learn makes breaking changes to its internal utilities, those libraries have historically duplicated the same compatibility code. This package factors out those utilities into a single maintained source.

The package tracks scikit-learn's breaking changes across versions (currently supporting 1.2 and later) and provides stable import paths for functions that have moved, changed signatures, or been renamed. Developers can import from sklearn-compat instead of directly from scikit-learn's private modules, and the package handles version detection and fallbacks automatically.

Use it for:

- Maintain a scikit-learn-compatible estimator library that needs to support multiple scikit-learn versions without duplicating compatibility shims.
- Migrate code from scikit-learn 1.6+ where validate_data moved and force_all_finite was deprecated while keeping backward compatibility.
- Use DataFrame type-checking utilities consistently across scikit-learn versions where they were added at different times.
- Adapt test helpers like _convert_container that changed parameter names between scikit-learn versions without forking test code.
- Support multiple scikit-learn versions in a library without vendoring or maintaining your own compatibility layer.

## Worth the install?

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

Provides backward-compatible wrappers for scikit-learn private utilities across multiple versions, allowing third-party libraries to support scikit-learn >= 1.2 without duplicating compatibility code.

Yes, if you maintain a scikit-learn-compatible library. It eliminates duplicate compatibility code across projects and is actively maintained to track scikit-learn changes. For end users of scikit-learn itself, there is no direct benefit—install only if a dependency requires it.

## Install

pip install sklearn-compat
uv add sklearn-compat
poetry add sklearn-compat

## Installing sklearn-compat

Before you install: Low install friction with a single runtime dependency on scikit-learn. Active maintenance with a release 68 days ago and ongoing development.

License in practice: Permissive license allows use in both open-source and commercial projects without significant restrictions.

Quickstart:

pip install sklearn-compat

from sklearn_compat.utils._dataframe import is_pandas_df
from sklearn_compat.utils.validation import validate_data

is_pandas_df(X)
X_validated = validate_data(estimator, X=X, ensure_all_finite=True)

Requires scikit-learn >= 1.2 to be installed; designed for library developers writing estimators, not end users.

Verify before relying:

- Whether the package will continue tracking all future scikit-learn breaking changes beyond current releases
- Whether vendoring the package is recommended over the PyPI dependency for production use

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 2.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

scikit-learn version compatibility, sklearn private API wrapper, multi-version sklearn support, sklearn estimator compatibility, cross-version sklearn utilities, sklearn-compatible, version-compatibility

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