Sample Size Requirements for Popular Classification Algorithms in Tabular Clinical Data: Empirical Study.

ABSTRACT:

Background

The performance of a classification algorithm eventually reaches a point of diminishing returns, where the additional sample added does not improve the results. Thus, there is a need to determine an optimal sample size that maximizes performance while accounting for computational burden or budgetary concerns.

Objective

This study aimed to determine optimal sample sizes and the relationships between sample size and dataset-level characteristics over a variety of binary classification algorithms.

Methods

A total of 16 large open-source datasets were collected, each containing a binary clinical outcome. Furthermore, 4 machine learning algorithms were assessed: XGBoost (XGB), random forest (RF), logistic regression (LR), and neural networks (NNs). For each data

SUBMITTER: Silvey S 

PROVIDER: S-EPMC11688588 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature