{"id":19448486,"url":"https://github.com/neurodata/sporf","last_synced_at":"2026-03-11T01:31:04.708Z","repository":{"id":36303449,"uuid":"81963350","full_name":"neurodata/SPORF","owner":"neurodata","description":"This is the implementation of Sparse Projection Oblique Randomer Forest","archived":false,"fork":false,"pushed_at":"2024-05-03T12:58:26.000Z","size":113720,"stargazers_count":98,"open_issues_count":101,"forks_count":45,"subscribers_count":9,"default_branch":"staging","last_synced_at":"2025-05-07T20:09:59.267Z","etag":null,"topics":["classification","cpp","decision-trees","python","r","random-forest"],"latest_commit_sha":null,"homepage":"https://neurodata.io/forests/","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/neurodata.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":".github/CONTRIBUTING.md","funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-02-14T16:07:10.000Z","updated_at":"2025-01-03T15:49:27.000Z","dependencies_parsed_at":"2024-11-10T16:31:46.954Z","dependency_job_id":"ffbab61d-3343-42a6-8bce-bbc2a6a28080","html_url":"https://github.com/neurodata/SPORF","commit_stats":{"total_commits":446,"total_committers":12,"mean_commits":"37.166666666666664","dds":0.5201793721973094,"last_synced_commit":"a7a3c7e6df457b722de86d7254f8a7724b27978f"},"previous_names":["neurodata/rerf"],"tags_count":7,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2FSPORF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2FSPORF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2FSPORF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2FSPORF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/neurodata","download_url":"https://codeload.github.com/neurodata/SPORF/tar.gz/refs/heads/staging","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252949279,"owners_count":21830152,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification","cpp","decision-trees","python","r","random-forest"],"created_at":"2024-11-10T16:27:11.825Z","updated_at":"2025-12-13T19:04:49.391Z","avatar_url":"https://github.com/neurodata.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [SPORF/RerF](https://neurodata.io/sporf)\n\n[![arXiv shield](https://img.shields.io/badge/arXiv-1506.03410-red.svg?style=flat)](https://arxiv.org/abs/1506.03410)\n[![PyPI version](https://badge.fury.io/py/rerf.svg)](https://badge.fury.io/py/rerf)\n[![CRAN Status Badge](https://www.r-pkg.org/badges/version/rerf)](https://cran.r-project.org/package=rerf)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2595524.svg)](https://doi.org/10.5281/zenodo.2595524)\n[![dockerhub](https://img.shields.io/badge/Hub.Docker-NeuroData%3ARerF-%232181E7.svg)](https://hub.docker.com/r/neurodata/rerf)\n[![Gigantum](https://img.shields.io/badge/Gigantum-View%20Project-593C5E?link=https://gigantum.com\u0026link=https://gigantum.com/neurodata/sporf-demo)](https://gigantum.com/neurodata/sporf-demo)\n![Downloads shield](https://img.shields.io/pypi/dm/rerf.svg)\n\n\nSPORF -- 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).\n\nThe 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.\n\n\n# Packages \n\n## [packedForest (C++)](packedForest/README.md)\n- Memory optimized C++ implementation of RandomForest and RerF.\n\n### [Py-RerF](Python/README.md)\n- Python bindings to packedForest.\n\n## [R-RerF](R-Project/README.md)\n- The R and C++ implemetation of RerF.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fsporf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneurodata%2Fsporf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fsporf/lists"}