Improving random forest predictions in small datasets from two-phase sampling designs.

ABSTRACT:

Background

While random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a small number of cases-a common situation in biomedical studies, which often have rare outcomes and covariates whose measurement is resource-intensive.

Methods

Using an immunologic marker dataset from a phase III HIV vaccine efficacy trial, we seek to optimize random forest prediction performance using combinations of variable screening, class balancing, weighting, and hyperparameter tuning.

Results

Our experiments show that while class balancing helps improve random forest prediction performance when variable screening is not applied, class balancing has a negative

SUBMITTER: Han S 

PROVIDER: S-EPMC8607560 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature