  This paper is devoted to model selection in logistic regression. We extend
the model selection principle introduced by Birg\'e and Massart (2001) to
logistic regression model. This selection is done by using penalized maximum
likelihood criteria. We propose in this context a completely data-driven
criteria based on the slope heuristics. We prove non asymptotic oracle
inequalities for selected estimators. Theoretical results are illustrated
through simulation studies.
