  In many cases, feature selection is often more complicated than identifying a
single subset of input variables that would together explain the output. There
may be interactions that depend on contextual information, i.e., variables that
reveal to be relevant only in some specific circumstances. In this setting, the
contribution of this paper is to extend the random forest variable importances
framework in order (i) to identify variables whose relevance is
context-dependent and (ii) to characterize as precisely as possible the effect
of contextual information on these variables. The usage and the relevance of
our framework for highlighting context-dependent variables is illustrated on
both artificial and real datasets.
