https://github.com/neurodata/sporf

This is the implementation of Sparse Projection Oblique Randomer Forest
https://github.com/neurodata/sporf

classification cpp decision-trees python r random-forest

Last synced: 2 months ago
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This is the implementation of Sparse Projection Oblique Randomer Forest

• Host: GitHub
• URL: https://github.com/neurodata/sporf
• Owner: neurodata
• Created: 2017-02-14T16:07:10.000Z (almost 8 years ago)
• Default Branch: staging
• Last Pushed: 2024-05-03T12:58:26.000Z (9 months ago)
• Last Synced: 2024-10-08T10:55:10.366Z (4 months ago)
• Topics: classification, cpp, decision-trees, python, r, random-forest
• Language: C++
• Homepage: https://neurodata.io/forests/
• Size: 108 MB
• Stars: 97
• Watchers: 10
• Forks: 46
• Open Issues: 101
• Metadata Files:
  • Readme: README.md
  • Contributing: .github/CONTRIBUTING.md

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README

        
# [SPORF/RerF](https://neurodata.io/sporf)

[![arXiv shield](https://img.shields.io/badge/arXiv-1506.03410-red.svg?style=flat)](https://arxiv.org/abs/1506.03410)

[![PyPI version](https://badge.fury.io/py/rerf.svg)](https://badge.fury.io/py/rerf)

[![CRAN Status Badge](https://www.r-pkg.org/badges/version/rerf)](https://cran.r-project.org/package=rerf)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2595524.svg)](https://doi.org/10.5281/zenodo.2595524)

[![dockerhub](https://img.shields.io/badge/Hub.Docker-NeuroData%3ARerF-%232181E7.svg)](https://hub.docker.com/r/neurodata/rerf)

[![Gigantum](https://img.shields.io/badge/Gigantum-View%20Project-593C5E?link=https://gigantum.com&link=https://gigantum.com/neurodata/sporf-demo)](https://gigantum.com/neurodata/sporf-demo)

![Downloads shield](https://img.shields.io/pypi/dm/rerf.svg)

SPORF -- sparse projection oblique randomer forests (aka RerF, Randomer Forest or Random Projection Forests) -- is an algorithm developed by [Tomita et al. (2016)](https://arxiv.org/abs/1506.03410) which is similar to Random Forest-Random Combination (Forest-RC) developed by [Breiman (2001)](https://doi.org/10.1023/A:1010933404324).

The difference between the two algorithms is where the random linear combinations occur: Forest-RC combines features at the tree level whereas RerF combines features at the node level.

# Packages 

## [packedForest (C++)](packedForest/README.md)

- Memory optimized C++ implementation of RandomForest and RerF.

### [Py-RerF](Python/README.md)

- Python bindings to packedForest.

## [R-RerF](R-Project/README.md)

- The R and C++ implemetation of RerF.