Deep Forest (DF) 21 (Support for Python 3.10+)

Community Maintained Fork of Deep Forest

This repository is a community-maintained copy of the original Deep Forest (DF21) project. We do not claim credit for the underlying research or implementation; our goal is to keep the project usable on modern Python versions (e.g., Python 3.10+) while the upstream repository has seen minimal activity for a long time (with one recent try to move to py310+ but with CI/CD failing). If the upstream maintainers prioritise these updates, we are happy to contribute everything back via pull request.

See more in #1

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

This fork is published on PyPI as deep-forest-py310. You can install it with:

pip install deep-forest-py310

If you specifically want to use the original upstream project (which may not support modern Python versions), you can instead install:

pip install deep-forest

Quickstart

Classification

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

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

@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