{"id":16780090,"url":"https://github.com/hk669/hyperparameter-optimization","last_synced_at":"2026-01-03T06:34:06.737Z","repository":{"id":250041848,"uuid":"660974729","full_name":"Hk669/Hyperparameter-Optimization","owner":"Hk669","description":"The AdaBoost algorithm is an ensemble learning method that combines multiple weak learners (base estimators) to create a stronger predictive model.","archived":false,"fork":false,"pushed_at":"2023-07-01T11:53:15.000Z","size":118,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-23T06:32:34.349Z","etag":null,"topics":["adaboostclassifier","decision-trees","decisiontreeclassifier","entropy","gini-index","hyperparameter-optimization","hyperparameter-tuning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Hk669.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2023-07-01T11:46:06.000Z","updated_at":"2023-07-10T12:48:49.000Z","dependencies_parsed_at":null,"dependency_job_id":"ad510b5f-7976-451a-b186-92e56360060d","html_url":"https://github.com/Hk669/Hyperparameter-Optimization","commit_stats":null,"previous_names":["hk669/hyperparameter-optimization"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hk669%2FHyperparameter-Optimization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hk669%2FHyperparameter-Optimization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hk669%2FHyperparameter-Optimization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hk669%2FHyperparameter-Optimization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Hk669","download_url":"https://codeload.github.com/Hk669/Hyperparameter-Optimization/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243926072,"owners_count":20369910,"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":["adaboostclassifier","decision-trees","decisiontreeclassifier","entropy","gini-index","hyperparameter-optimization","hyperparameter-tuning"],"created_at":"2024-10-13T07:33:59.710Z","updated_at":"2026-01-03T06:34:06.694Z","avatar_url":"https://github.com/Hk669.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AdaBoostClassifier with DecisionTreeClassifier\n## Overview\nThis README provides information on how to use the AdaBoostClassifier from scikit-learn library with DecisionTreeClassifier as the base estimator. The AdaBoost algorithm is an ensemble learning method that combines multiple weak learners (base estimators) to create a stronger predictive model.\n\n## Further Customization\nYou can further customize the AdaBoostClassifier by modifying the parameters and using different base estimators. For example, you can try different values for n_estimators, learning_rate, or change the base_estimator to other classifiers like RandomForestClassifier or SVC.\n\n## Conclusion\nUsing the AdaBoostClassifier with DecisionTreeClassifier as the base estimator can be a powerful technique for solving classification problems. It combines multiple weak learners to create a stronger predictive model. By following the steps outlined in this README, you can easily implement and customize the AdaBoost algorithm for your own datasets.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhk669%2Fhyperparameter-optimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhk669%2Fhyperparameter-optimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhk669%2Fhyperparameter-optimization/lists"}