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All Projects → Freemanzxp → Gbdt_simple_tutorial

Freemanzxp / Gbdt_simple_tutorial

Licence: apache-2.0
python实现GBDT的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，庖丁解牛地理解GBDT。Gradient Boosting Decision Trees regression, dichotomy and multi-classification are realized based on python, and the details of algorithm flow are displayed, interpreted and visualized to help readers better understand Gradient Boosting Decision

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GBDT_Simple_Tutorial（梯度提升树简易教程）

简介

利用python实现GBDT算法的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，便于读者庖丁解牛地理解GBDT。

项目进度：

  • [x] 回归
  • [x] 二分类
  • [x] 多分类
  • [x] 可视化

算法原理以及公式推导请前往blog：GBDT算法原理以及实例理解

依赖环境

  • 操作系统：Windows/Linux
  • 编程语言：Python3
  • Python库：pandas、PIL、pydotplus， 其中pydotplus库会自动调用Graphviz，所以需要去Graphviz官网下载graphviz的-2.38.msi ，先安装，再将安装目录下的bin添加到系统环境变量，此时如果再报错可以重启计算机。详细过程不再描述，网上很多解答。

文件结构

  • | - GBDT 主模块文件夹
  • | --- gbdt.py 梯度提升算法主框架
  • | --- decision_tree.py 单颗树生成，包括节点划分和叶子结点生成
  • | --- loss_function.py 损失函数
  • | --- tree_plot.py 树的可视化
  • | - example.py 回归/二分类/多分类测试文件

运行指南

  • 回归测试：

    python example.py --model = regression

  • 二分类测试：

    python example.py --model = binary_cf

  • 多分类测试：

    python example.py --model = multi_cf

  • 其他可配置参数：lr -- 学习率, trees -- 构建的决策树数量即迭代次数,
    depth -- 决策树的深度, count -- 决策树节点分裂的最小数据数量, is_log -- 是否打印树的生成过程, is_plot -- 是否可视化树的结构.

  • 结果文件： 运行后会生成results文件夹,里面包含了每棵树的内部结构和生成日志

结果展示

仅展示最后所有树的集合，具体每棵树的详细信息望读者自行运行代码~

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