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.
Whenever one used tree-based machine learning approaches such as Random Forest or GBDT, DF21 may offer a new powerful option.
For a quick start, please refer to How to Get Started. For a detailed guidance on parameter tunning, please refer to Parameters Tunning.
The package is available via PyPI using:
pip install deep-forestfrom 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 %- Documentation
- Deep Forest: [Paper]
- Keynote at AISTATS 2019: [Slides]
@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,
Author = {Zhi-Hua Zhou and Ji Feng},
Booktitle = {IJCAI},
Pages = {3553-3559},
Title = {{Deep Forest:} Towards an alternative to deep neural networks},
Year = {2017}}The lead developer and maintainer of DF21 is Mr. Yi-Xuan Xu. Before the release, it has been used internally in the LAMDA Group, Nanjing University, China.