Welcome to Sklearn-Wrap's documentation¶
Sklearn-wrap enables you to wrap any Python class into a Scikit-Learn compatible estimator without rewriting your code. Whether you're integrating XGBoost's Booster API, custom gradient descent algorithms, or third-party machine learning libraries, Sklearn-Wrap provides the glue layer that makes them work seamlessly with Scikit-Learn's ecosystem.
With Sklearn-Wrap, you gain immediate access to GridSearchCV for hyperparameter tuning, meta estimators like Pipeline for composable workflows, joblib for serialization, and declarative YAML configuration via EstimatorConfig while maintaining your original implementation. This enables data scientists to achieve Scikit-Learn compatibility without sacrificing custom logic or performance.
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Get Started in 5 Minutes
Install Sklearn-Wrap and create your first wrapper. Learn the basic pattern and immediately gain meta estimator compatibility.
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How-to Guides
Task-oriented guides for wrapping classes, validating parameters, YAML configuration, GridSearchCV integration, and nesting wrappers.
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Understand the Design
Learn why the delegation pattern works, how
_fit_contextmanages the lifecycle, and the trade-offs of composition over inheritance. -
API Reference
Complete reference for all Sklearn-Wrap classes, functions, and configuration options.
License¶
This project is licensed under the terms of the Apache-2.0 License.
Acknowledgements¶
This project is maintained by stateful-y, an ML consultancy specializing in data science & engineering. If you're interested in collaborating or learning more about our services, please visit our website.
