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Abstract
  • 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.
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