{"id":22072948,"url":"https://github.com/jhylin/ml2-2_random_forest","last_synced_at":"2025-03-23T19:25:37.140Z","repository":{"id":197172538,"uuid":"698098221","full_name":"jhylin/ML2-2_random_forest","owner":"jhylin","description":"Machine learning series 2.2 on random forest","archived":false,"fork":false,"pushed_at":"2024-05-03T01:35:51.000Z","size":2956,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-29T02:48:28.221Z","etag":null,"topics":["cheminformatics","machine-learning","random-forest-classifier","random-forest-regression"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jhylin.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","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":"2023-09-29T06:44:58.000Z","updated_at":"2024-05-03T01:35:54.000Z","dependencies_parsed_at":null,"dependency_job_id":"6b3cbe4d-8dc8-45b8-99bb-6e3ab4fa2db0","html_url":"https://github.com/jhylin/ML2-2_random_forest","commit_stats":null,"previous_names":["jhylin/ml2-2_random_forest"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jhylin%2FML2-2_random_forest","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jhylin%2FML2-2_random_forest/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jhylin%2FML2-2_random_forest/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jhylin%2FML2-2_random_forest/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jhylin","download_url":"https://codeload.github.com/jhylin/ML2-2_random_forest/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245155997,"owners_count":20569796,"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":["cheminformatics","machine-learning","random-forest-classifier","random-forest-regression"],"created_at":"2024-11-30T21:16:07.621Z","updated_at":"2025-03-23T19:25:37.112Z","avatar_url":"https://github.com/jhylin.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"#### **Machine learning series 2.2 - Random forest**\n\nThis repository currently holds most of the files used in this random forest (RF) project. RF is another commonly used machine learning (ML) algorithm in drug discovery. I've attempted to use a deeper-dive style to explore the following two posts, but there were actually a lot of other things to be covered, so the these posts were not completely comprehensive, but they'd be snapshots of how I approached it at the time prior to the published dates. \n\nThere are currently two posts on RF:\n\n1. [Random forest](https://jhylin.github.io/Data_in_life_blog/posts/17_ML2-2_Random_forest/1_random_forest.html) (it's on a RF regressor, although named as RF only) or its Jupyter notebook version is available above (1_random_forest.ipynb). This work was run in a virtual environment of Python 3.10.\n\n2. [Random forest classifier](https://jhylin.github.io/Data_in_life_blog/posts/17_ML2-2_Random_forest/2_random_forest_classifier.html) or its Jupyter notebook version is available above (2_random_forest_classifier.ipynb). This work was also run in a virtual environment of Python 3.10.\n\nBoth posts can be reached from my [blog](https://jhylin.github.io/Data_in_life_blog/) as well.\n\n##### **Datasets**\n\nThe first post used the same set of data derived from ML series 2.1 (decision tree) was used. For details about how I've derived this dataset, please visit series 2.1 posts [1](https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/1_data_col_prep.html) and [2](https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/2_data_prep_tran.html).\n\nThe second post used [chembl_downloader](https://github.com/cthoyt/chembl-downloader) instead, for details please visit the post.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhylin%2Fml2-2_random_forest","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjhylin%2Fml2-2_random_forest","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhylin%2Fml2-2_random_forest/lists"}