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.

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 <https://deep-forest.readthedocs.io/en/latest/how_to_get_started.html>. For a detailed guidance on parameter tunning, please refer to Parameters Tunning <https://deep-forest.readthedocs.io/en/latest/parameters_tunning.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.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 <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-hua_zhou.pdf>__

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|

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