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使用工具变量的条件分类树

Conditional Classification Trees Using Instrumental Variables
课程网址: http://videolectures.net/ida07_tutore_cct/  
主讲教师: Valerio Aniello Tutore
开课单位: 那不勒斯大学
开课时间: 2007-10-08
课程语种: 英语
中文简介:
本文的框架是使用分类树进行监督学习。两类变量在分类规则的定义中起作用,即响应变量和一组预测因子。树分类器是由预测空间的递归划分建立的,这样就可以提供与响应类相关的内部同构对象组。在下文中,我们考虑了工具变量对变量或对象分层所起的作用。这就引入了一种基于树的条件分类方法。讨论了两种特殊情况下的多判别树和部分可预测树的生长。这些方法分别采用判别分析和可预测性测度。在实际案例研究中,将展示它们的有用性的经验证据。
课程简介: The framework of this paper is supervised learning using classification trees. Two types of variables play a role in the definition of the classification rule, namely a response variable and a set of predictors. The tree classifier is built up by a recursive partitioning of the prediction space such to provide internally homogeneous groups of objects with respect to the response classes. In the following, we consider the role played by an instrumental variable to stratify either the variables or the objects. This yields to introduce a tree-based methodology for conditional classification. Two special cases will be discussed to grow multiple discriminant trees and partial predictability trees. These approaches use discriminant analysis and predictability measures respectively. Empirical evidence of their usefulness will be shown in real case studies.
关 键 词: 监督学习; 分类树; 响应变量; 预测因子; 预测空间
课程来源: 视频讲座网
最后编审: 2020-09-28:heyf
阅读次数: 57