{"id":22072945,"url":"https://github.com/jhylin/ml2-1_decision_tree","last_synced_at":"2025-03-23T19:25:38.081Z","repository":{"id":189642770,"uuid":"681006907","full_name":"jhylin/ML2-1_decision_tree","owner":"jhylin","description":"Machine learning series 2.1 on decision tree","archived":false,"fork":false,"pushed_at":"2024-09-09T04:59:50.000Z","size":772,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-29T02:48:30.698Z","etag":null,"topics":["cheminformatics","classification-models","decision-tree-regression","logistic-regression","machine-learning"],"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":"2023-08-21T04:08:35.000Z","updated_at":"2024-09-10T04:17:15.000Z","dependencies_parsed_at":null,"dependency_job_id":"4ae7d28b-5b06-4c25-aa21-541ae10a27d1","html_url":"https://github.com/jhylin/ML2-1_decision_tree","commit_stats":null,"previous_names":["jhylin/ml2-1_decision_tree"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jhylin%2FML2-1_decision_tree","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jhylin%2FML2-1_decision_tree/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jhylin%2FML2-1_decision_tree/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jhylin%2FML2-1_decision_tree/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jhylin","download_url":"https://codeload.github.com/jhylin/ML2-1_decision_tree/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245155996,"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","classification-models","decision-tree-regression","logistic-regression","machine-learning"],"created_at":"2024-11-30T21:16:07.451Z","updated_at":"2025-03-23T19:25:38.056Z","avatar_url":"https://github.com/jhylin.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"#### **Machine learning series 2.1 - Decision tree**\n\nMachine learning (ML) series 2 will be largely about tree models which encompass decision tree, random forest and boosted trees. I'm planning to have deeper dives into using each of these tree models on drug discovery data from ChEMBL database. I'll also attempt to save different tree models in different repositories as I work on them later.\n\nThis repository will hold most of the data and files used in ML series 2.1 for building a decision tree model. All the code used here was run in Python 3.9 venv and available in .ipynb or .qmd files depending on the preferences.\n\nThis series was presented as 3 posts with links provided below:\n\n[Post 1](https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/1_data_col_prep.html) - data collection from ChEMBL database using web resource client in Python, with initial data preprocessing\n\n[Post 2](https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/2_data_prep_tran.html) - more data preprocessing and transformation to reach the final dataset prior to model building\n\n[Post 3](https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/3_model_build.html) - estimating experimental errors and building decision tree model using scikit-learn\n\nOther way to access above posts is directly through my [blog](https://jhylin.github.io/Data_in_life_blog/).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhylin%2Fml2-1_decision_tree","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjhylin%2Fml2-1_decision_tree","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhylin%2Fml2-1_decision_tree/lists"}