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https://github.com/LAMDA-NJU/Deep-Forest

An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)
https://github.com/LAMDA-NJU/Deep-Forest

deep-forest ensemble-learning machine-learning python random-forest

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An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)

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README 

        
Deep Forest (DF) 21

===================



|github|_ |readthedocs|_ |codecov|_ |python|_ |pypi|_ |style|_



.. |github| image:: https://github.com/LAMDA-NJU/Deep-Forest/workflows/DeepForest-CI/badge.svg

.. _github: https://github.com/LAMDA-NJU/Deep-Forest/actions



.. |readthedocs| image:: https://readthedocs.org/projects/deep-forest/badge/?version=latest

.. _readthedocs: https://deep-forest.readthedocs.io



.. |codecov| image:: https://codecov.io/gh/LAMDA-NJU/Deep-Forest/branch/master/graph/badge.svg?token=5BVXOT8RPO

.. _codecov: https://codecov.io/gh/LAMDA-NJU/Deep-Forest

    

.. |python| image:: https://img.shields.io/pypi/pyversions/deep-forest

.. _python: https://pypi.org/project/deep-forest/



.. |pypi| image:: https://img.shields.io/pypi/v/deep-forest?color=blue

.. _pypi: https://pypi.org/project/deep-forest/



.. |style| image:: https://img.shields.io/badge/code%20style-black-000000.svg

.. _style: https://github.com/psf/black



**DF21** is an implementation of `Deep Forest `__ 2021.2.1. It is designed to have the following advantages:



- **Powerful**: Better accuracy than existing tree-based ensemble methods.

- **Easy to Use**: Less efforts on tunning parameters.

- **Efficient**: Fast training speed and high efficiency.

- **Scalable**: Capable of handling large-scale data.



DF21 offers an effective & powerful option to the tree-based machine learning algorithms such as Random Forest or GBDT.



For a quick start, please refer to `How to Get Started `__. For a detailed guidance on parameter tunning, please refer to `Parameters Tunning `__.



DF21 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 `__ for details.



Installation

------------



DF21 can be installed using pip via `PyPI `__  which is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes. Refer `this `__ for the documentation of pip. Use this command to download DF21 :



.. code-block:: bash



    pip install deep-forest



Quickstart

----------



Classification

**************



.. code-block:: python



    from sklearn.datasets import load_digits

    from sklearn.model_selection import train_test_split

    from sklearn.metrics import accuracy_score



    from deepforest import CascadeForestClassifier



    X, y = load_digits(return_X_y=True)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

    model = CascadeForestClassifier(random_state=1)

    model.fit(X_train, y_train)

    y_pred = model.predict(X_test)

    acc = accuracy_score(y_test, y_pred) * 100

    print("\nTesting Accuracy: {:.3f} %".format(acc))

    >>> Testing Accuracy: 98.667 %



Regression

**********



.. code-block:: python



    from sklearn.datasets import load_boston

    from sklearn.model_selection import train_test_split

    from sklearn.metrics import mean_squared_error



    from deepforest import CascadeForestRegressor



    X, y = load_boston(return_X_y=True)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

    model = CascadeForestRegressor(random_state=1)

    model.fit(X_train, y_train)

    y_pred = model.predict(X_test)

    mse = mean_squared_error(y_test, y_pred)

    print("\nTesting MSE: {:.3f}".format(mse))

    >>> Testing MSE: 8.068



Resources

---------



* `Documentation `__

* Deep Forest: `[Conference] `__ | `[Journal] `__

* Keynote at AISTATS 2019: `[Slides] `__



Reference

---------



.. code-block:: latex



    @article{zhou2019deep,

        title={Deep forest},

        author={Zhi-Hua Zhou and Ji Feng},

        journal={National Science Review},

        volume={6},

        number={1},

        pages={74--86},

        year={2019}}



    @inproceedings{zhou2017deep,

        title = {{Deep Forest:} Towards an alternative to deep neural networks},

        author = {Zhi-Hua Zhou and Ji Feng},

        booktitle = {IJCAI},

        pages = {3553--3559},

        year = {2017}}



Thanks to all our contributors

------------------------------



|contributors|



.. |contributors| image:: https://contributors-img.web.app/image?repo=LAMDA-NJU/Deep-Forest

.. _contributors: https://github.com/LAMDA-NJU/Deep-Forest/graphs/contributors