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.

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.

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

Documentation
Deep Forest: [Conference] | [Journal]
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,
    title = {{Deep Forest:} Towards an alternative to deep neural networks},
    author = {Zhi-Hua Zhou and Ji Feng},
    booktitle = {IJCAI},
    pages = {3553--3559},
    year = {2017}}

Thanks to all our contributors

contributors

Metadata

Release files for deep-forest 0.1.7

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for deep-forest 0.1.7
File	Interpreter	ABI	Platform	Reset
deep_forest-0.1.7-cp39-cp39-win_amd64.whl 555.1 kB Oct 1, 2022	CPython 3.9	CPython 3.9	Windows x86-64	Details
deep_forest-0.1.7-cp39-cp39-manylinux2014_aarch64.whl 2.8 MB Oct 1, 2022	CPython 3.9	CPython 3.9	Linux glibc 2.17+ ARM64	Details
deep_forest-0.1.7-cp39-cp39-manylinux2010_x86_64.whl 2.6 MB Oct 1, 2022	CPython 3.9	CPython 3.9	Linux glibc 2.12+ x86-64	Details
deep_forest-0.1.7-cp39-cp39-manylinux1_x86_64.whl 2.6 MB Oct 1, 2022	CPython 3.9	CPython 3.9	Linux glibc 2.5+ x86-64	Details
deep_forest-0.1.7-cp39-cp39-macosx_10_9_x86_64.whl 568.7 kB Oct 1, 2022	CPython 3.9	CPython 3.9	macOS 10.9+ x86-64	Details
deep_forest-0.1.7-cp38-cp38-win_amd64.whl 556.9 kB Oct 1, 2022	CPython 3.8	CPython 3.8	Windows x86-64	Details
deep_forest-0.1.7-cp38-cp38-manylinux2014_aarch64.whl 2.9 MB Oct 1, 2022	CPython 3.8	CPython 3.8	Linux glibc 2.17+ ARM64	Details
deep_forest-0.1.7-cp38-cp38-manylinux2010_x86_64.whl 2.8 MB Oct 1, 2022	CPython 3.8	CPython 3.8	Linux glibc 2.12+ x86-64	Details
deep_forest-0.1.7-cp38-cp38-manylinux1_x86_64.whl 2.8 MB Oct 1, 2022	CPython 3.8	CPython 3.8	Linux glibc 2.5+ x86-64	Details
deep_forest-0.1.7-cp38-cp38-macosx_10_9_x86_64.whl 557.8 kB Oct 1, 2022	CPython 3.8	CPython 3.8	macOS 10.9+ x86-64	Details
deep_forest-0.1.7-cp37-cp37m-win_amd64.whl 548.1 kB Oct 1, 2022	CPython 3.7	CPython 3.7 pymalloc	Windows x86-64	Details
deep_forest-0.1.7-cp37-cp37m-manylinux2014_aarch64.whl 2.7 MB Oct 1, 2022	CPython 3.7	CPython 3.7 pymalloc	Linux glibc 2.17+ ARM64	Details
deep_forest-0.1.7-cp37-cp37m-manylinux2010_x86_64.whl 2.5 MB Oct 1, 2022	CPython 3.7	CPython 3.7 pymalloc	Linux glibc 2.12+ x86-64	Details
deep_forest-0.1.7-cp37-cp37m-manylinux1_x86_64.whl 2.5 MB Oct 1, 2022	CPython 3.7	CPython 3.7 pymalloc	Linux glibc 2.5+ x86-64	Details
deep_forest-0.1.7-cp37-cp37m-macosx_10_9_x86_64.whl 560.7 kB Oct 1, 2022	CPython 3.7	CPython 3.7 pymalloc	macOS 10.9+ x86-64	Details

Total release size: 27.5 MB

Release files / deep_forest-0.1.7-cp39-cp39-win_amd64.whl

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