Feature Selection with the<b>Boruta</b>Package

This article describes a <b>R</b> package <b>Boruta</b>, implementing a novel feature selection algorithm for finding emph{all relevant variables}. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The <b>Boruta</b> package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.