Summary of Eipy: An Open-source Python Package For Multi-modal Data Integration Using Heterogeneous Ensembles, by Jamie J. R. Bennett et al.
eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles
by Jamie J. R. Bennett, Aviad Susman, Yan Chak Li, Gaurav Pandey
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors introduce eipy, an open-source Python package for developing and comparing multi-modal heterogeneous ensembles for classification. This framework simultaneously provides a rigorous and user-friendly approach to evaluating the performance of various data integration and predictive modeling methods using nested cross-validation. The package leverages scikit-learn-like estimators as components to build multi-modal predictive models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Eipy is a new tool that helps scientists combine different types of data to make better predictions. It compares different ways to do this and chooses the best one. This makes it easier for researchers to find the right method for their project. The package uses a familiar framework, scikit-learn, to build models. |
Keywords
* Artificial intelligence * Classification * Multi modal
