# mlscorecheck: Testing the consistency of reported performance scores and experiments in machine learning

arXiv: https://arxiv.org/abs/2311.07541 · HTML: https://mlsanity.com/paper/2311.07541/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1605.01397/) (2016)

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

- [Enumerating the k-fold configurations in multi-class classification problems](https://mlsanity.com/paper/2401.13843/) (2024)

## Abstract (arXiv)

Addressing the reproducibility crisis in artificial intelligence through the validation of reported experimental results is a challenging task. It necessitates either the reimplementation of techniques or a meticulous assessment of papers for deviations from the scientific method and best statistical practices. To facilitate the validation of reported results, we have developed numerical techniques capable of identifying inconsistencies between reported performance scores and various experimental setups in machine learning problems, including binary/multiclass classification and regression. These consistency tests are integrated into the open-source package mlscorecheck, which also provides specific test bundles designed to detect systematically recurring flaws in various fields, such as retina image processing and synthetic minority oversampling.