ArticleBMC bioinformatics2025
optRF: Optimising random forest stability by determining the optimal number of trees.
Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Multi-metric evaluation and parametric optimization of stochastic gradient boosting machines for genomic prediction and selection in wheat (Triticum aestivum) breeding.G3 (Bethesda, Md.) · 2026Article
- Machine learning for land use change analysis in environmental protection areas.Environmental monitoring and assessment · 2026Article
- Article
- Normalized cumulative gain as an alternative evaluation measure for genomic selection models.Genetics, selection, evolution : GSE · 2025Article
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- Identification of Shared Diagnostic Biomarkers LCN2 and DUOX2 for Inflammatory Bowel Disease and Cholangiocarcinoma Through Integrative Bioinformatics and Machine Learning.Journal of inflammation research · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning is frequently used to make decisions based on big data. Among these techniques, random forest is particularly prominent. Although random forest is known to have many advantages, one aspect that is often overseen is that it is a non-deterministic method that can produce different models using the same input data. This can have severe consequences on decision-making processes. In this study, we introduce a method to quantify the impact of non-determinism on predictions, variable importance estimates, and decisions based on the predictions or variable importance estimates. Our findings demonstrate that increasing the number of trees in random forests enhances the stability in a non-linear way while computation time increases linearly. Consequently, we conclude that there exists an optimal number of trees for any given data set that maximises the stability without unnecessarily increasing the computation time. Based on these findings, we have developed the R package optRF which models the relationship between the number of trees and the stability of random forest, providing recommendations for the optimal number of trees for any given data set.
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Registered trials
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