# Sample selection from a given dataset to validate machine learning models

arXiv: https://arxiv.org/abs/2104.14401 · HTML: https://mlsanity.com/paper/2104.14401/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 27 Apr 2021.

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

- [Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)](https://mlsanity.com/paper/2102.01564/) (2021)
- [Support points](https://mlsanity.com/paper/1609.01811/) (2016)

## Abstract (arXiv)

The selection of a validation basis from a full dataset is often required in industrial use of supervised machine learning algorithm. This validation basis will serve to realize an independent evaluation of the machine learning model. To select this basis, we propose to adopt a "design of experiments" point of view, by using statistical criteria. We show that the "support points" concept, based on Maximum Mean Discrepancy criteria, is particularly relevant. An industrial test case from the company EDF illustrates the practical interest of the methodology.