eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles
This package addresses the need for accessible tools in multi-modal data integration for researchers and practitioners, though it is incremental as it builds on existing scikit-learn-like estimators.
The paper introduces eipy, an open-source Python package for building multi-modal heterogeneous ensembles for classification, providing a user-friendly framework to compare and select the best-performing methods using nested cross-validation.
In this paper, we introduce eipy--an open-source Python package for developing effective, multi-modal heterogeneous ensembles for classification. eipy simultaneously provides both a rigorous, and user-friendly framework for comparing and selecting the best-performing multi-modal data integration and predictive modeling methods by systematically evaluating their performance using nested cross-validation. The package is designed to leverage scikit-learn-like estimators as components to build multi-modal predictive models. An up-to-date user guide, including API reference and tutorials, for eipy is maintained at https://eipy.readthedocs.io . The main repository for this project can be found on GitHub at https://github.com/GauravPandeyLab/eipy .