Published August 25, 2017
| Version v1
Dataset
Open
iterative Random Forests data and analyses
- 1. Department of Biological Statistics and Computational Biology, Cornell University
- 2. Statistics Department, University of California, Berkeley
- 3. Centre for Computational Biology, School of Biosciences, University of Birmingham; Molecular Ecosystems Biology Department, Lawrence Berkeley National Laboratory; Preminon, LLC
- 4. Statistics Department, University of California, Berkeley; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
Description
This repository contains scripts to run the simulations and case studies described in: iterative Random Forests to discover predictive and stable high-order interactions.
Files
iRF_analyses.zip
Files
(3.1 MB)
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md5:96518741d9c24db82520fd6af326ee9d
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