eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles

TL;DR

eipy is an open-source Python package that facilitates the development and comparison of multi-modal heterogeneous ensemble classifiers, providing a user-friendly framework with rigorous evaluation methods.

Contribution

It introduces a comprehensive, scikit-learn-compatible Python package for multi-modal data integration and ensemble modeling with systematic performance evaluation.

Findings

Enables effective multi-modal data integration for classification.

Provides a systematic framework for model comparison and selection.

Supports rigorous performance evaluation using nested cross-validation.

Abstract

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 .