Deep Forest (DF) 21 ===================

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DF21 is an implementation of Deep Forest <https://arxiv.org/pdf/1702.08835.pdf>__ 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.

For a quick start, please refer to How to Get Started <https://deep-forest.readthedocs.io/en/latest/howtogetstarted.html>. For a detailed guidance on parameter tunning, please refer to Parameters Tunning <https://deep-forest.readthedocs.io/en/latest/parameterstunning.html>.

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 <https://github.com/kingfengji/gcForest>__ for details.

Installation

DF21 can be installed using pip via PyPI <https://pypi.org/project/deep-forest/> which is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes. Refer this <https://pypi.org/project/pip/> 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.modelselection import traintest_split from sklearn.metrics import accuracy_score

from deepforest import CascadeForestClassifier

X, y = loaddigits(returnX_y=True) Xtrain, Xtest, ytrain, ytest = traintestsplit(X, y, random_state=1) model = CascadeForestClassifier(random_state=1) model.fit(Xtrain, ytrain) ypred = model.predict(Xtest) acc = accuracyscore(ytest, 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.modelselection import traintest_split from sklearn.metrics import meansquarederror

from deepforest import CascadeForestRegressor

X, y = loadboston(returnX_y=True) Xtrain, Xtest, ytrain, ytest = traintestsplit(X, y, random_state=1) model = CascadeForestRegressor(random_state=1) model.fit(Xtrain, ytrain) ypred = model.predict(Xtest) mse = meansquarederror(ytest, ypred) print("\nTesting MSE: {:.3f}".format(mse)) >>> Testing MSE: 8.068

Resources

• Documentation <https://deep-forest.readthedocs.io/>__
• Deep Forest: [Conference] <https://www.ijcai.org/proceedings/2017/0497.pdf> | [Journal] <https://academic.oup.com/nsr/article-pdf/6/1/74/30336169/nwy108.pdf>
• Keynote at AISTATS 2019: [Slides] <https://aistats.org/aistats2019/0-AISTATS2019-slides-zhi-huazhou.pdf>_

.. 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

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