Most existing feature selection methods are insufficient for analytic

purposes as soon as high dimensional data or redundant sensor signals are dealt

with since features can be selected due to spurious effects or correlations

rather than causal effects. To support the finding of causal features in

biomedical experiments, we hereby present FRI, an open source Python library

that can be used to identify all-relevant variables in linear classification

and (ordinal) regression problems. Using the recently proposed feature

relevance method, FRI is able to provide the base for further general

experimentation or in specific can facilitate the search for alternative

biomarkers. It can be used in an interactive context, by providing model

manipulation and visualization methods, or in a batch process as a filter

method.