  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.
