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DF21 is an implementation of Deep Forest 2021年2月1日. 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.
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
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 %
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
- Documentation
- Deep Forest: [Conference] | [Journal]
- 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,
title = {{Deep Forest:} Towards an alternative to deep neural networks},
author = {Zhi-Hua Zhou and Ji Feng},
booktitle = {IJCAI},
pages = {3553--3559},
year = {2017}}