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 .

该论文介绍了eipy——一个用于开发有效的、多模态的异构集成分类器的开源Python包。eipy同时提供一个严谨且用户友好的框架，通过对其性能进行系统评估并使用嵌套交叉验证来比较和选择最佳的多模态数据融合和预测建模方法。该包旨在利用类似于scikit-learn的估计器作为组件来构建多模态的预测模型。eipy的最新用户指南包括API参考和教程的维护可以在此https URL找到。该项目的主要存储库可以在GitHub的此https URL找到。

eipy：一个多模态数据集成的开源Python软件包，使用异构集成