# Methodology to Create Analysis-Naive Holdout Records as well as Train and Test Records for Machine Learning Analyses in Healthcare

arXiv: https://arxiv.org/abs/2205.03987 · HTML: https://mlsanity.com/paper/2205.03987/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 3 of 3)

- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [The Shape of Learning Curves: a Review](https://mlsanity.com/paper/2103.10948/) (2021)
- [Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial](https://mlsanity.com/paper/2008.13690/) (2020)

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

- [Validation of massively-parallel adaptive testing using dynamic control matching](https://mlsanity.com/paper/2305.01334/) (2023)

## Abstract (arXiv)

It is common for researchers to holdout data from a study pool to be used for external validation as well as for future research, and the same desire is true to those using machine learning modeling research. For this discussion, the purpose of the holdout sample it is preserve data for research studies that will be analysis-naive and randomly selected from the full dataset. Analysis-naive are records that are not used for testing or training machine learning (ML) models and records that do not participate in any aspect of the current machine learning study. The methodology suggested for creating holdouts is a modification of k-fold cross validation, which takes into account randomization and efficiently allows a three-way split (holdout, test and training) as part of the method without forcing. The paper also provides a working example using set of automated functions in Python and some scenarios for applicability in healthcare.