Optimal ratio for data splitting

Georgia Institute of Technology

Indexed inarxivcrossref

Abstract

Abstract 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 training/testing splitting ratio is , where is the number of parameters in a linear regression model that explains the data well.

Citation impact

742
total citations
FWCI
91.98
Percentile
100%
References
31
Citations per year

Authors

1

Topics & keywords

Keywords
  • Computer science
  • Training set
  • Linear regression
  • Machine learning
  • Data mining
  • Statistical hypothesis testing
  • Artificial intelligence
  • Statistics
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