Storms prediction : Logistic regression vs random forest for unbalanced data

Anne Ruiz-Gazen (1, 2) , Nathalie Villa (1)

1 IMT - Institut de Mathématiques de Toulouse UMR5219
2 GREMAQ - Groupe de recherche en économie mathématique et quantitative

The aim 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 forest on a real data set with unbalanced classes.

Mots clés: logistic regression; unbalanced data; storm prediction

Domaines: Applications [stat.AP]; Statistiques [math.ST]; Théorie [stat.TH]

Anne Ruiz-Gazen, Nathalie Villa. Storms prediction : Logistic regression vs random forest for unbalanced data. Case Studies in Business, Industry and Government Statistics, 2007, 1 (2), pp.91-101. ⟨hal-00270176⟩