{"id":13689159,"url":"https://github.com/LAMDA-NJU/Deep-Forest","last_synced_at":"2025-05-01T23:32:34.277Z","repository":{"id":43890437,"uuid":"333274594","full_name":"LAMDA-NJU/Deep-Forest","owner":"LAMDA-NJU","description":"An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)","archived":false,"fork":false,"pushed_at":"2024-04-29T16:42:57.000Z","size":448,"stargazers_count":914,"open_issues_count":32,"forks_count":160,"subscribers_count":20,"default_branch":"master","last_synced_at":"2024-11-09T22:32:22.529Z","etag":null,"topics":["deep-forest","ensemble-learning","machine-learning","python","random-forest"],"latest_commit_sha":null,"homepage":"https://deep-forest.readthedocs.io","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/LAMDA-NJU.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGELOG.rst","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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-01-27T02:05:09.000Z","updated_at":"2024-11-09T03:38:16.000Z","dependencies_parsed_at":"2024-01-17T06:12:21.238Z","dependency_job_id":"1c9d8603-2778-4c1e-be22-ed2e9aae1cb7","html_url":"https://github.com/LAMDA-NJU/Deep-Forest","commit_stats":null,"previous_names":[],"tags_count":7,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LAMDA-NJU%2FDeep-Forest","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LAMDA-NJU%2FDeep-Forest/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LAMDA-NJU%2FDeep-Forest/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LAMDA-NJU%2FDeep-Forest/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LAMDA-NJU","download_url":"https://codeload.github.com/LAMDA-NJU/Deep-Forest/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224282146,"owners_count":17285778,"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":["deep-forest","ensemble-learning","machine-learning","python","random-forest"],"created_at":"2024-08-02T15:01:35.999Z","updated_at":"2024-11-12T13:30:55.064Z","avatar_url":"https://github.com/LAMDA-NJU.png","language":"Python","readme":"Deep Forest (DF) 21\n===================\n\n|github|_ |readthedocs|_ |codecov|_ |python|_ |pypi|_ |style|_\n\n.. |github| image:: https://github.com/LAMDA-NJU/Deep-Forest/workflows/DeepForest-CI/badge.svg\n.. _github: https://github.com/LAMDA-NJU/Deep-Forest/actions\n\n.. |readthedocs| image:: https://readthedocs.org/projects/deep-forest/badge/?version=latest\n.. _readthedocs: https://deep-forest.readthedocs.io\n\n.. |codecov| image:: https://codecov.io/gh/LAMDA-NJU/Deep-Forest/branch/master/graph/badge.svg?token=5BVXOT8RPO\n.. _codecov: https://codecov.io/gh/LAMDA-NJU/Deep-Forest\n    \n.. |python| image:: https://img.shields.io/pypi/pyversions/deep-forest\n.. _python: https://pypi.org/project/deep-forest/\n\n.. |pypi| image:: https://img.shields.io/pypi/v/deep-forest?color=blue\n.. _pypi: https://pypi.org/project/deep-forest/\n\n.. |style| image:: https://img.shields.io/badge/code%20style-black-000000.svg\n.. _style: https://github.com/psf/black\n\n**DF21** is an implementation of `Deep Forest \u003chttps://arxiv.org/pdf/1702.08835.pdf\u003e`__ 2021.2.1. It is designed to have the following advantages:\n\n- **Powerful**: Better accuracy than existing tree-based ensemble methods.\n- **Easy to Use**: Less efforts on tunning parameters.\n- **Efficient**: Fast training speed and high efficiency.\n- **Scalable**: Capable of handling large-scale data.\n\nDF21 offers an effective \u0026 powerful option to the tree-based machine learning algorithms such as Random Forest or GBDT.\n\nFor a quick start, please refer to `How to Get Started \u003chttps://deep-forest.readthedocs.io/en/latest/how_to_get_started.html\u003e`__. For a detailed guidance on parameter tunning, please refer to `Parameters Tunning \u003chttps://deep-forest.readthedocs.io/en/latest/parameters_tunning.html\u003e`__.\n\nDF21 is optimized for what a tree-based ensemble excels at (i.e., tabular data), if you want to use the multi-grained scanning part to better handle structured data like images, please refer to the `origin implementation \u003chttps://github.com/kingfengji/gcForest\u003e`__ for details.\n\nInstallation\n------------\n\nDF21 can be installed using pip via `PyPI \u003chttps://pypi.org/project/deep-forest/\u003e`__  which is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes. Refer `this \u003chttps://pypi.org/project/pip/\u003e`__ for the documentation of pip. Use this command to download DF21 :\n\n.. code-block:: bash\n\n    pip install deep-forest\n\nQuickstart\n----------\n\nClassification\n**************\n\n.. code-block:: python\n\n    from sklearn.datasets import load_digits\n    from sklearn.model_selection import train_test_split\n    from sklearn.metrics import accuracy_score\n\n    from deepforest import CascadeForestClassifier\n\n    X, y = load_digits(return_X_y=True)\n    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n    model = CascadeForestClassifier(random_state=1)\n    model.fit(X_train, y_train)\n    y_pred = model.predict(X_test)\n    acc = accuracy_score(y_test, y_pred) * 100\n    print(\"\\nTesting Accuracy: {:.3f} %\".format(acc))\n    \u003e\u003e\u003e Testing Accuracy: 98.667 %\n\nRegression\n**********\n\n.. code-block:: python\n\n    from sklearn.datasets import load_boston\n    from sklearn.model_selection import train_test_split\n    from sklearn.metrics import mean_squared_error\n\n    from deepforest import CascadeForestRegressor\n\n    X, y = load_boston(return_X_y=True)\n    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n    model = CascadeForestRegressor(random_state=1)\n    model.fit(X_train, y_train)\n    y_pred = model.predict(X_test)\n    mse = mean_squared_error(y_test, y_pred)\n    print(\"\\nTesting MSE: {:.3f}\".format(mse))\n    \u003e\u003e\u003e Testing MSE: 8.068\n\nResources\n---------\n\n* `Documentation \u003chttps://deep-forest.readthedocs.io/\u003e`__\n* Deep Forest: `[Conference] \u003chttps://www.ijcai.org/proceedings/2017/0497.pdf\u003e`__ | `[Journal] \u003chttps://academic.oup.com/nsr/article-pdf/6/1/74/30336169/nwy108.pdf\u003e`__\n* Keynote at AISTATS 2019: `[Slides] \u003chttps://aistats.org/aistats2019/0-AISTATS2019-slides-zhi-hua_zhou.pdf\u003e`__\n\nReference\n---------\n\n.. code-block:: latex\n\n    @article{zhou2019deep,\n        title={Deep forest},\n        author={Zhi-Hua Zhou and Ji Feng},\n        journal={National Science Review},\n        volume={6},\n        number={1},\n        pages={74--86},\n        year={2019}}\n\n    @inproceedings{zhou2017deep,\n        title = {{Deep Forest:} Towards an alternative to deep neural networks},\n        author = {Zhi-Hua Zhou and Ji Feng},\n        booktitle = {IJCAI},\n        pages = {3553--3559},\n        year = {2017}}\n\nThanks to all our contributors\n------------------------------\n\n|contributors|\n\n.. |contributors| image:: https://contributors-img.web.app/image?repo=LAMDA-NJU/Deep-Forest\n.. _contributors: https://github.com/LAMDA-NJU/Deep-Forest/graphs/contributors\n","funding_links":[],"categories":["梯度提升和树模型","Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FLAMDA-NJU%2FDeep-Forest","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FLAMDA-NJU%2FDeep-Forest","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FLAMDA-NJU%2FDeep-Forest/lists"}