DF21 Documentation

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. This package is actively being developed, and any help would be welcomed. Please check the homepage on Gitee or Github for details.

Guidepost

  • For a quick start, please refer to How to Get Started.

  • For a guidance on tunning parameters for DF21, please refer to Parameters Tunning.

  • For a comparison between DF21 and other tree-based ensemble methods, please refer to Experiments.

Installation

DF21 can be installed using pip via PyPI which is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes. Refer this for the documentation of pip. Use this command to download DF21 :

$ 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

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}}

For Developers

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.