# Design choice and machine learning model performances

arXiv: https://arxiv.org/abs/2201.10239 · HTML: https://mlsanity.com/paper/2201.10239/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 61 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Replication or exploration? Sequential design for stochastic simulation experiments](https://mlsanity.com/paper/1710.03206/) (2017)
- [Robust Experimental Designs for Model Calibration](https://mlsanity.com/paper/2008.00547/) (2020)
- [A permutation approach for ranking of multivariate populations](https://mlsanity.com/paper/1304.5453/) (2013)

## Built on by (most cited first, 2 of 2)

- [TechOps: Technical Documentation Templates for the AI Act](https://mlsanity.com/paper/2508.08804/) (2025)
- [The use of cross validation in the analysis of designed experiments](https://mlsanity.com/paper/2506.14593/) (2025)

## Abstract (arXiv)

An increasing number of publications present the joint application of Design of Experiments (DOE) and machine learning (ML) as a methodology to collect and analyze data on a specific industrial phenomenon. However, the literature shows that the choice of the design for data collection and model for data analysis is often not driven by statistical or algorithmic advantages, thus there is a lack of studies which provide guidelines on what designs and ML models to jointly use for data collection and analysis. This article discusses the choice of design in relation to the ML model performances. A study is conducted that considers 12 experimental designs, 7 families of predictive models, 7 test functions that emulate physical processes, and 8 noise settings, both homoscedastic and heteroscedastic. The results of the research can have an immediate impact on the work of practitioners, providing guidelines for practical applications of DOE and ML.