Deep Forest is a general ensemble framework that uses tree-based ensemble algorithms such as Random Forest. 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.

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-forest

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 %

• Documentation
• Deep Forest: [Paper]
• Keynote at AISTATS 2019: [Slides]

@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}}