Local permutation importances for random forests

Abstract

The topic of this thesis is to study and compare local ranking methods applied to ensembles of trees with the aim to interpret each prediction of a model. This work contributes to the understanding of the ensembles of trees through the study of methods that locally identify the variables that are important for a prediction. The second contribution of the thesis is the further application of local ranking methods to the Gene Regulatory Network inference problem. The results of the local methods are compared with ground-truth data inferred from a simulator and their performances are compared to the state of the art method of that field.