Scikit-Lego
Scikit-Lego is an open-source Python package that extends the scikit-learn ecosystem by providing additional custom transformers, metrics, and models compatible with scikit-learn pipelines. It allows users to integrate these components seamlessly alongside standard scikit-learn tools, facilitating the construction of more diverse machine learning pipelines without the need to implement these components from scratch. The project is maintained collaboratively by multiple companies in the Netherlands and adheres to code quality and testing standards aligned with scikit-learn guidelines. Scikit-Lego is freely available and can be installed via pip or conda.
An open-source Python package offering scikit-learn compatible custom transformers, metrics, and models for enhanced machine learning pipelines.
Enhancing Machine Learning Pipelines
Data scientists using scikit-learn who require additional transformers or metrics not included in the core library can incorporate Scikit-Lego components to extend pipeline functionality.
pip install scikit-lego or conda install -c conda-forge scikit-lego to install the package.from sklego.transformers import RandomAdder.Pipeline([('scale', StandardScaler()), ('random_noise', RandomAdder()), ('model', LogisticRegression())]).