# FRI -- Feature Relevance Intervals for Interpretable and Interactive Data Exploration

## TL;DR

FRI is an open source Python library designed to identify all relevant features in linear classification and regression tasks, aiding causal feature discovery in high-dimensional data and supporting interactive data exploration.

## Contribution

The paper introduces FRI, a novel tool that provides comprehensive feature relevance intervals for linear models, enhancing interpretability and interaction in high-dimensional data analysis.

## Key findings

- Enables identification of all relevant features in linear models.
- Supports interactive and batch data exploration.
- Facilitates discovery of causal features in biomedical data.

## 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.