Decision tree methods: applications for classification and prediction Translated title: 用于分类与预测的决策树分析

Summary

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

Translated abstract

概述

决策树是一种常用的数据挖掘方法，用于多变量分析时建立分类系统或制定预测结果变量的算法。此方法将一个数据群分割成分枝状节段，构造出包括根节点、内部节点和叶节点的倒置形树状模型。该算法运用非参数方法，不需要套用任何复杂的参数模型就能有效地处理大型复杂的数据库。当样本足够大时，可将研究数据分为训练数据集和验证数据集。使用训练数据集构建决策树模型，使用验证数据集来决定树的适合大小，以获得最优模型。本文介绍了构建决策树的常用算法（包括CART，C4.5，CHAID和QUEST），并描述了SPSS和SAS软件中将树结构可视化的程序。

中文全文

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