FRI -- Feature Relevance Intervals for Interpretable and Interactive Data Exploration
This addresses the challenge of feature selection for biomedical researchers dealing with redundant sensor signals, though it is incremental as it builds on existing feature relevance methods.
The authors tackled the problem of identifying causal features in high-dimensional biomedical data by developing FRI, an open-source Python library that identifies all-relevant variables in linear classification and regression problems, supporting interactive exploration and batch processing.
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.