Evaluation of a decided sample size in machine learning applications.

ABSTRACT:

Background

An appropriate sample size is essential for obtaining a precise and reliable outcome of a study. In machine learning (ML), studies with inadequate samples suffer from overfitting of data and have a lower probability of producing true effects, while the increment in sample size increases the accuracy of prediction but may not cause a significant change after a certain sample size. Existing statistical approaches using standardized mean difference, effect size, and statistical power for determining sample size are potentially biased due to miscalculations or lack of experimental details. This study aims to design criteria for evaluating sample size in ML studies. We examined the average and grand effect sizes and the performance of five ML methods using simulated datasets

SUBMITTER: Rajput D 

PROVIDER: S-EPMC9926644 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature