mlsanityData to 7 Oct 2026

Optimal Ratio for Data Splitting

arXiv 2202.03326 ↗First posted 7 Feb 2022stat.ML cs.LGcited by 17

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Abstract (arXiv)

It is common to split a dataset into training and testing sets before fitting a statistical or machine learning model. However, there is no clear guidance on how much data should be used for training and testing. In this article we show that the optimal splitting ratio is √p:1, where p is the number of parameters in a linear regression model that explains the data well.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 7 Feb 2022.

Builds on

4 of 28 references.

References · 4

  1. 2016Support pointscited by 39
  2. 2020SPlit: An Optimal Method for Data Splittingcited by 15
  3. 2021Data Twinningcited by 10
  4. 2021Test Set Sizing Via Random Matrix Theorycited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 5; 2025: 7; 2026: 1.

17 papers.

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