决策树算法入门_Python实现决策树

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使用python实现决策树

使用python实现决策树

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使用python实现决策树

使用python实现决策树

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课程视频时间

42 分钟
语言

英文
课程类型

线上

USD 10.00

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课程介绍

决策树是一种十分常用的分类方法，是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率，评价项目风险，判断其可行性的决策分析方法。在机器学习中，决策树是一个预测模型，他代表的是对象属性与对象值之间的一种映射关系。Entropy=系统的凌乱程度，使用算法ID3, C4.5和C5.0生成树算法使用熵。本课程介绍了决策树基本概念，算法及如何通过Python的Scikit-learn库构建决策树。

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有Python开发使用经验的，想要学习机器学习的工程师

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Apsara Clouder—大数据：使用python实现决策树
考试类型：

线上
语言:

英文
考试时间：

30 分钟
考试机会：

2次

相关课题

使用python实现决策树

通过本课程学习，了解什么是决策树，决策树的三种算法以及如何通过Scikit-learn构建决策树。

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课程内容简介
决策树概述
决策树算法
决策树例子
课程回顾

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