{"id":21601552,"url":"https://github.com/vmware-samples/efficient-multiclass-classification","last_synced_at":"2025-09-07T01:36:25.236Z","repository":{"id":98086892,"uuid":"477133139","full_name":"vmware-samples/efficient-multiclass-classification","owner":"vmware-samples","description":"Duet is a scikit-learn classifier for resource-efficient multiclass classification that incorporates the advantages of bagging and boosting decision-tree-based ensemble methods (DTEMs) by using two classifiers instead of a monolithic one. A simple bagging model is trained using the entire training dataset and is responsible for capturing the easier concepts. Then, a boosting model is trained using only a fraction of the dataset representing the concepts the bagging model finds hard.","archived":false,"fork":false,"pushed_at":"2022-04-20T09:28:16.000Z","size":26,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-09-07T01:36:24.734Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","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/vmware-samples.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","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":"2022-04-02T18:02:41.000Z","updated_at":"2024-11-15T03:00:12.000Z","dependencies_parsed_at":"2023-05-23T14:45:14.876Z","dependency_job_id":null,"html_url":"https://github.com/vmware-samples/efficient-multiclass-classification","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/vmware-samples/efficient-multiclass-classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vmware-samples%2Fefficient-multiclass-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vmware-samples%2Fefficient-multiclass-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vmware-samples%2Fefficient-multiclass-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vmware-samples%2Fefficient-multiclass-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vmware-samples","download_url":"https://codeload.github.com/vmware-samples/efficient-multiclass-classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vmware-samples%2Fefficient-multiclass-classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273986611,"owners_count":25202704,"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","status":"online","status_checked_at":"2025-09-06T02:00:13.247Z","response_time":2576,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":[],"created_at":"2024-11-24T19:09:52.683Z","updated_at":"2025-09-07T01:36:25.218Z","avatar_url":"https://github.com/vmware-samples.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Duet scikit classifier (v1.0)\n\n\n## Overview\n\nDuet is a decision tree ensemble method based multiclass classification \nframework that offers a more efficient resource usage while preserving and even \nimproving the classification accuracy in comparison to standard monolithic \nmodels.\n\nDuet is based on a small bagging ensemble model and a booting model.\u003cbr/\u003e\nThe current implementation of Duet is based on Random Forest and XGBoost.\n\n## Documentation\n\nMore details about the Duet can be found in the following paper:\u003cbr/\u003e\n\"Efficient Multiclass Classification with Duet\" [EuroMLSys '22]\u003cbr/\u003e\n\u003chttps://dl.acm.org/doi/abs/10.1145/3517207.3526970\u003e\u003cbr/\u003e\n\u003chttps://euromlsys.eu/pdf/euromlsys22-final4.pdf\u003e\n\n## Files:\n\n#### duet_classifier.py \nDuet scikit classifier\n\n#### classification_example.py\nBasic classification example by Duet\n\n#### grid_search_example.py\nBasic grid search example with Duet\n\n## Prerequisities:\nnumpy\u003cbr/\u003e\npandas\u003cbr/\u003e\nskleran\u003cbr/\u003e\nxgboost\u003cbr/\u003e\n\n\nor alternatively, run:\u003cbr/\u003e\n$ pip3 install -r requirements.txt\n\n## Contributing\n\nThe efficient-multiclass-classification project team welcomes contributions from the community. Before you start working with efficient-multiclass-classification, please\nread our [Developer Certificate of Origin](https://cla.vmware.com/dco). All contributions to this repository must be\nsigned as described on that page. Your signature certifies that you wrote the patch or have the right to pass it on\nas an open-source patch. For more detailed information, refer to [CONTRIBUTING.md](CONTRIBUTING.md).\n\n## License\n\nBSD-3 License \n\n## Contact us\n\nFor more information, support and advanced examples contact:\u003cbr/\u003e\nYaniv Ben-Itzhak, [ybenitzhak@vmware.com](mailto:ybenitzhak@vmware.com)\u003cbr/\u003e\nShay Vargaftik, [shayv@vmware.com](mailto:shayv@vmware.com)\u003cbr/\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvmware-samples%2Fefficient-multiclass-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvmware-samples%2Fefficient-multiclass-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvmware-samples%2Fefficient-multiclass-classification/lists"}