# FSEVAL: Feature Selection Evaluation Toolbox and Dashboard

arXiv: https://arxiv.org/abs/2604.18227 · HTML: https://mlsanity.com/paper/2604.18227/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version](https://mlsanity.com/paper/2501.09591/) (2025)

## Built on by (most cited first, 3 of 3)

- [Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection](https://mlsanity.com/paper/2605.22973/) (2026)
- [An Empirical Study of Feature Selection Granularity](https://mlsanity.com/paper/2607.24145/) (2026)
- [Towards Truly Unsupervised Evaluation of Feature Selection -- Extended Version](https://mlsanity.com/paper/2608.12057/) (2026)

## Abstract (arXiv)

Feature selection is a fundamental machine learning and data mining task, involved with discriminating redundant features from informative ones. It is an attempt to address the curse of dimensionality by removing the redundant features, while unlike dimensionality reduction methods, preserving explainability. Feature selection is conducted in both supervised and unsupervised settings, with different evaluation metrics employed to determine which feature selection algorithm is the best. In this paper, we propose FSEVAL, a feature selection evaluation toolbox accompanied with a visualization dashboard, with the goal to make it easy to comprehensively evaluate feature selection algorithms. FSEVAL aims to provide a standardized, unified, evaluation and visualization toolbox to help the researchers working in the field, conduct extensive and comprehensive evaluation of feature selection algorithms with ease.