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README
BSD-3-Clause

深度森林DF21

项目介绍

本项目中的DF21是深度森林的2021.2.1实现版本。深度森林是基于决策树的深度学习模型。使用树模型学习技术(如随机森林、GBDT等)的应用都可以尝试使用DF21。它具有以下的优势:

  • 拥有比其他基于决策树的集成学习方法更好的性能
  • 拥有更少的超参数,并且无需大量的调参
  • 训练效率高,并且能够处理大规模的数据集

安装教程

深度森林的最新稳定版本已经发布在PyPi上,您可以通过以下命令安装:

$ pip install deep-forest

关于Nightly-Build版本以及如何对源代码进行编译请参考官网的安装说明

使用说明

深度森林采用了与Scikit-Learn类似的API设计。例如,下面的代码片段展示了在安装完成后,如何将深度森林利用在一个简单的数字分类数据集上:

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 %

关于深度森林在实际应用中的性能,以及与其他基于决策树的集成学习方法的对比,请参考官网的实验部分

相关资源

相关信息

本项目目前主要由徐轶轩进行开发和维护。在正式发布之前,它已经在LAMDA内部经过测试和使用。

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