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

arXiv: https://arxiv.org/abs/1903.00719 · HTML: https://mlsanity.com/paper/1903.00719/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets](https://mlsanity.com/paper/2206.02056/) (2022)

## Abstract (arXiv)

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.