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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","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"}