文章基本信息

标题：Predicting Storms: Logistic Regression versus Random Forests for Unbalanced Data
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作者：Anne Ruiz-Gazen ; Nathalie Villa
期刊名称：Case Studies in Business, Industry and Government Statistics
印刷版ISSN：2152-372X
出版年度：2007
卷号：1
期号：2
页码：91-101
出版社：Bentley University
摘要：The goal of this study is to compare two supervised classification methods on a crucial meteorological problem. The data consist of satellite measurements of cloud systems which are to be classified either in convective or non convective systems. Convective cloud systems correspond to lightning and detecting such systems is of main importance for thunderstorm monitoring and warning. Because the problem is highly unbalanced, we consider specific performance criteria and different strategies. This case study can be used in an advanced course of data mining in order to illustrate the use of logistic regression and random forests on a real data set with unbalanced classes.