ArticlePeerJ. Computer science2024
Out of (the) bag-encoding categorical predictors impacts out-of-bag samples.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
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- Machine Learning-Enhanced MEC Sensors with Feature Engineering for Quantitative Analysis of Multi-Component Toxicants.Biosensors · 2026Article
- fNIRS-Based characterization of adolescent depression using dynamic functional connectivity biomarkers in a verbal fluency task.BMC psychiatry · 2026Article
- Perioperative phase angle decline is associated with postoperative sarcopenia in esophageal cancer: a longitudinal cohort study with internal validation.Frontiers in nutrition · 2026Article
- Endophenotype Research in Epilepsy Across Time.Brain sciences · 2025Article
- INFO-RF-based fault diagnosis and analysis method for busbars.Scientific reports · 2025Article
- Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest.Healthcare (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Performance of random forest classification models is often assessed and interpreted using out-of-bag (OOB) samples. Observations which are OOB when a tree is trained may serve as a test set for that tree and predictions from the OOB observations used to calculate OOB error and variable importance measures (VIM). OOB errors are popular because they are fast to compute and, for large samples, are a good estimate of the true prediction error. In this study, we investigate how target-based
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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.