Trial reportBMC medical informatics and decision making2021
Improving random forest predictions in small datasets from two-phase sampling designs.
Trial report in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.Journal of affective disorders · 2026Trial
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- Risk Factors and Prediction Models for Nonalcoholic Fatty Liver Disease Based on Random Forest.Computational and mathematical methods in medicine · 2022Trial
- Scalable prediction of suicidal risk in university students: a three steps machine learning approach in university settings.Scientific reports · 2026Article
- Rice LAI estimation using UAV-based multi-parameter fusion at the booting stage.Scientific reports · 2026Article
- Article
- Identification of key influencing factors of health information literacy in COPD patients: a cross-sectional study using a random forest model.BMC pulmonary medicine · 2026Article
- Research on the Assessment of Dairy Cow Dry Matter Intake Using ITSO-Optimized Stacking Ensemble Learning.Animals : an open access journal from MDPI · 2026Article
- Pruning tree forest and re-sampling for class imbalanced problem.Scientific reports · 2026Article
- RF-SVR-based prediction methodology for metal tube-bending rebound: Handling non-uniformity and limited sample challenges.PloS one · 2026Article
- Integration of machine learning and viscoelastic testing to improve survival prediction in horses experiencing acute abdominal pain at a veterinary teaching hospital.Equine veterinary journal · 2026Article
- Prediction of Lithium Therapeutic Target Attainment in Adolescents Using an XGBoost Model: A Single-Center Retrospective Study.Drug design, development and therapy · 2026Article
- Predicting the Risk of Burnout Syndrome Using Korean Occupational Stress Scale (KOSS): A Machine Learning Approach.Safety and health at work · 2025Article
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
- Comprehensive Epigenome-Wide Profiling Reveals Distinctive DNA Methylation Signatures and Potential Prognostic Biomarkers in Mexican Pediatric B-ALL.International journal of molecular sciences · 2025Article
- Relative importance of temporal and location features in predicting smoking events.NPJ digital medicine · 2025Article
- Predicting cisplatin response in cholangiocarcinoma patients using chromosome pattern and related gene expression.Scientific reports · 2025Article
- Serotype specific pneumococcal vaccine effectiveness in children with sickle cell disease: A two-decade analysis.Vaccine · 2025Article
- Machine-learning-based cost prediction models for inpatients with mental disorders in China.BMC psychiatry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundWhile 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.
methodsUsing 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.
resultsOur experiments show that while class balancing helps improve random forest prediction performance when variable screening is not applied, class balancing has a negative impact on performance in the presence of variable screening. The impact of the weighting similarly depends on whether variable screening is applied. Hyperparameter tuning is ineffective in situations with small sample sizes. We further show that random forests under-perform generalized linear models for some subsets of markers, and prediction performance on this dataset can be improved by stacking random forests and generalized linear models trained on different subsets of predictors, and that the extent of improvement depends critically on the dissimilarities between candidate learner predictions.
conclusionIn small datasets from two-phase sampling design, variable screening and inverse sampling probability weighting are important for achieving good prediction performance of random forests. In addition, stacking random forests and simple linear models can offer improvements over random forests.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.