{"id":34082302,"url":"https://github.com/vinpap/symbolic-learn","last_synced_at":"2026-03-10T11:32:32.463Z","repository":{"id":153033389,"uuid":"627573904","full_name":"vinpap/symbolic-learn","owner":"vinpap","description":"A symbolic regression model","archived":false,"fork":false,"pushed_at":"2023-10-27T07:35:36.000Z","size":221,"stargazers_count":4,"open_issues_count":6,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-12-16T14:26:03.921Z","etag":null,"topics":["genetic-algorithm","package","regression-models","sklearn-model","symbolic-regression"],"latest_commit_sha":null,"homepage":"","language":"Cython","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/vinpap.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2023-04-13T18:46:16.000Z","updated_at":"2024-09-23T04:39:24.000Z","dependencies_parsed_at":null,"dependency_job_id":"21af761b-4da1-4382-b66f-759114c9431c","html_url":"https://github.com/vinpap/symbolic-learn","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/vinpap/symbolic-learn","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinpap%2Fsymbolic-learn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinpap%2Fsymbolic-learn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinpap%2Fsymbolic-learn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinpap%2Fsymbolic-learn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vinpap","download_url":"https://codeload.github.com/vinpap/symbolic-learn/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vinpap%2Fsymbolic-learn/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30332310,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-10T05:25:20.737Z","status":"ssl_error","status_checked_at":"2026-03-10T05:25:17.430Z","response_time":106,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["genetic-algorithm","package","regression-models","sklearn-model","symbolic-regression"],"created_at":"2025-12-14T12:11:53.358Z","updated_at":"2026-03-10T11:32:32.454Z","avatar_url":"https://github.com/vinpap.png","language":"Cython","funding_links":[],"categories":[],"sub_categories":[],"readme":"Welcome to symbolic-learn's repository!\n========================================\n\nsymbolic-learn is a sklearn-compatible package that implements a symbolic regression model.\n\n\nWhat is symbolic regression?\n------------------------------\n\nSymbolic regression is a type of regression model that combines mathematical blocks to find the function that best fits the data. Here each function is represented as a binary tree like this one:\n\n.. image:: https://raw.githubusercontent.com/vinpap/symbolic-learn/master/docs/_static/genetic_program_tree.png\n   :alt: Function tree representation : image not found\n   :align: center\n\nThe model initially generates a random population of such functions. It then uses genetic programming techniques on it to find out the function that best fits our dataset.\nAs this model is based on `scikit-learn's \u003chttp://scikit-learn.org\u003e`_ base estimator, it can be used the same way you would use any sklearn model. Thus, you can use it in pipelines or apply fine-tuning techniques such as GridSearchCV on it.\n\nSymbolic regression is best used when you want to take a naive approach to solving a regression problem. Unlike most existing models, it does not come with an **a priori** specification of a model. Therefore it is a good idea to use it when you want to find out and understand the mathematical structures in your data. \n\nExample\n-----------------------------\n\nHere is how to instantiate and train a symbolic regression model::\n\n    from sblearn.models import SymbolicRegressor\n    model = SymbolicRegressor()\n    model.fit(X_train, y_train)\n\nAfter training your model, you can use access the fitted function's formula and function tree through the model's attributes `formulas` and `trees`. `Read the doc \u003chttps://symbolic-learn.readthedocs.io/en/latest/\u003e`_ for more information.\n\n\nInstallation\n---------------------------\n\nIn order to install the package, use this command::\n\n   pip install symbolic-learn\n\n*Note for Windows users*: Microsoft Visual C++ 2014 or higher is required!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvinpap%2Fsymbolic-learn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvinpap%2Fsymbolic-learn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvinpap%2Fsymbolic-learn/lists"}