{"id":13936150,"url":"https://github.com/levyben/DeepSuperLearner","last_synced_at":"2025-07-19T21:31:51.315Z","repository":{"id":201618975,"uuid":"129538501","full_name":"levyben/DeepSuperLearner","owner":"levyben","description":"DeepSuperLearner - Python implementation of the deep ensemble algorithm","archived":false,"fork":false,"pushed_at":"2018-06-22T06:23:45.000Z","size":189,"stargazers_count":136,"open_issues_count":3,"forks_count":23,"subscribers_count":11,"default_branch":"master","last_synced_at":"2024-11-27T04:30:40.927Z","etag":null,"topics":["classification-algorithm","ensemble-learning","machine-learning"],"latest_commit_sha":null,"homepage":null,"language":"Python","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/levyben.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2018-04-14T17:19:19.000Z","updated_at":"2024-01-04T16:22:17.000Z","dependencies_parsed_at":null,"dependency_job_id":"a9318052-3843-4890-99d2-bb837763c921","html_url":"https://github.com/levyben/DeepSuperLearner","commit_stats":null,"previous_names":["levyben/deepsuperlearner"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/levyben/DeepSuperLearner","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/levyben%2FDeepSuperLearner","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/levyben%2FDeepSuperLearner/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/levyben%2FDeepSuperLearner/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/levyben%2FDeepSuperLearner/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/levyben","download_url":"https://codeload.github.com/levyben/DeepSuperLearner/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/levyben%2FDeepSuperLearner/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266019657,"owners_count":23864916,"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":["classification-algorithm","ensemble-learning","machine-learning"],"created_at":"2024-08-07T23:02:25.197Z","updated_at":"2025-07-19T21:31:46.305Z","avatar_url":"https://github.com/levyben.png","language":"Python","readme":"# DeepSuperLearner (2018) in Python\r\nThis is a [sklearn](http://scikit-learn.org/stable/)\r\nimplementation of the machine-learning DeepSuperLearner algorithm, A Deep Ensemble method for Classification Problems.\r\n\r\nFor details about DeepSuperLearner please refer to the [https://arxiv.org/abs/1803.02323](https://arxiv.org/abs/1803.02323):\r\nDeep Super Learner: A Deep Ensemble for Classification Problems by Steven Young, Tamer Abdou, and Ayse Bener.\r\n\r\n### Installation and demo\r\n1. Clone this repository\r\n    ```bash\r\n    git clone https://github.com/levyben/DeepSuperLearner.git\r\n    ```\r\n\r\n2. Install the python library\r\n    ```bash\r\n    cd DeepSuperLearner\r\n    python setup.py install\r\n    ```\r\n\r\n### Example:\r\n```python\r\n    ERT_learner = ExtremeRandomizedTrees(n_estimators=200, max_depth=None, max_features=1)\r\n    kNN_learner = kNearestNeighbors(n_neighbors=11)\r\n    LR_learner = LogisticRegression()\r\n    RFC_learner = RandomForestClassifier(n_estimators=200, max_depth=None)\r\n    XGB_learner = XGBClassifier(n_estimators=200, max_depth=3, learning_rate=1.)\r\n    Base_learners = {'ExtremeRandomizedTrees':ERT_learner, 'kNearestNeighbors':kNN_learner, 'LogisticRegression':LR_learner,\r\n                     'RandomForestClassifier':RFC_learner, 'XGBClassifier':XGB_learner}    \r\n    np.random.seed(100)\r\n    X, y = datasets.make_classification(n_samples=1000, n_features=12,\r\n                                        n_informative=2, n_redundant=6)\r\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\r\n    DSL_learner = DeepSuperLearner(Base_learners)\r\n    DSL_learner.fit(X_train, y_train)\r\n    DSL_learner.get_precision_recall(X_test, y_test, show_graphs=True)    \r\n```\r\n\r\nSee deepSuperLearner/example.py for full example.\r\n\r\n![Alt text](example.PNG?raw=true \"Example results on sklearn simple binary classification data\")\r\n\r\n\r\n### Notes: \r\n1. For running example you need to install the XGB python lib as it is used as a base learner just as done in the paper.\r\n2. Although the algorithm is implemented for classification problems, it can be modified to perform on regression problems aswell.\r\n\r\n### TODO:\r\n- [x] Train on some sklearn data.\r\n- [ ] Restore paper classification results.\r\n\r\n","funding_links":[],"categories":["Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flevyben%2FDeepSuperLearner","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flevyben%2FDeepSuperLearner","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flevyben%2FDeepSuperLearner/lists"}