{"id":17255164,"url":"https://github.com/unixjunkie/orf","last_synced_at":"2025-08-21T05:43:18.703Z","repository":{"id":42006685,"uuid":"374870868","full_name":"UnixJunkie/orf","owner":"UnixJunkie","description":"OCaml Random Forests","archived":false,"fork":false,"pushed_at":"2023-01-10T00:23:28.000Z","size":1169,"stargazers_count":8,"open_issues_count":6,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-27T19:23:31.347Z","etag":null,"topics":["bagging","bootstrap-aggregation","classification","machine-learning","ocaml-library","out-of-bag","random-forests","regression","statistical-modeling"],"latest_commit_sha":null,"homepage":"","language":"OCaml","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/UnixJunkie.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-06-08T03:37:04.000Z","updated_at":"2022-05-18T21:54:03.000Z","dependencies_parsed_at":"2023-02-08T16:16:38.317Z","dependency_job_id":null,"html_url":"https://github.com/UnixJunkie/orf","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UnixJunkie%2Forf","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UnixJunkie%2Forf/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UnixJunkie%2Forf/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UnixJunkie%2Forf/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/UnixJunkie","download_url":"https://codeload.github.com/UnixJunkie/orf/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248830416,"owners_count":21168271,"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":["bagging","bootstrap-aggregation","classification","machine-learning","ocaml-library","out-of-bag","random-forests","regression","statistical-modeling"],"created_at":"2024-10-15T07:10:50.951Z","updated_at":"2025-04-14T05:40:57.936Z","avatar_url":"https://github.com/UnixJunkie.png","language":"OCaml","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ORF: OCaml Random Forests\n\nRandom Forests (RFs) are one of the workhorse of modern machine learning.\nEspecially, they cannot over-fit to the training set, are\nfast to train, predict fast, parallelize well and give you a reasonable model\neven without optimizing the model's default hyper-parameters.\nIn other words, it is hard to shoot yourself in the foot while\ntraining or exploiting a Random Forests model.\nIn comparison, with deep neural networks\nit is very easy to shoot yourself in the foot.\n\nUsing out of bag (OOB) samples, you can even get an idea\nof a RFs performance, without the need for a held out\n(test) dataset.\n\nTheir only drawback is that RFs, being an ensemble model,\ncannot predict values which are outside of the training set\nrange of values (this _is_ a serious limitation in case you\nare trying to optimize or minimize something in order to discover\noutliers, compared to your training set samples).\n\nFor the moment, this implementation will only consider a sparse vector of\nintegers as features. i.e. categorical variables will need to be\none-hot-encoded.\n\n# Bibliography\n\nBreiman, Leo. (1996). \"Bagging Predictors\". Machine learning, 24(2), 123-140.\n\nBreiman, Leo. (2001). \"Random Forests\". Machine learning, 45(1), 5-32.\n\nGeurts, P., Ernst, D., \u0026 Wehenkel, L. (2006). \"Extremely Randomized Trees\".\nMachine learning, 63(1), 3-42.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Funixjunkie%2Forf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Funixjunkie%2Forf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Funixjunkie%2Forf/lists"}