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

arXiv 2401.09582 ↗First posted 17 Jan 2024cs.LGcited by 0

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Abstract (arXiv)

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 .

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References · 5

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  3. 2019What Makes Training Multi-Modal Classification Networks Hard?cited by 340
  4. 2021Explaining a Series of Models by Propagating Shapley Valuescited by 37
  5. 2023MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learningcited by 4

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