Deep Forest (DF) 21

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.

Installation

The package is available via PyPI using:

pip install deep-forest

Quickstart

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 %

Resources

Documentation
Deep Forest: [Paper]
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,
    Author = {Zhi-Hua Zhou and Ji Feng},
    Booktitle = {IJCAI},
    Pages = {3553-3559},
    Title = {{Deep Forest:} Towards an alternative to deep neural networks},
    Year = {2017}}

Acknowledgement

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.

nkgfirecream / Deep-Forest