ArticleBMC medical informatics and decision making2022
Monte Carlo cross-validation for a study with binary outcome and limited sample size.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
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- Genetic dissection of protein content in cowpea using custom-made NIRS equations and GWAS as a model for nutritional breeding and undergraduate research training.G3 (Bethesda, Md.) · 2026Article
- Five-Year Risk Prediction Models for Peripheral Artery Disease in Patients With Chronic Kidney Disease.Kidney medicine · 2026Article
- Empowering atrial fibrillation detection with TabPFN and SHAP interpretation based on ECG-derived features: a dual-center temporal validation study.BMC cardiovascular disorders · 2026Article
- Metabolomics Highlights Reduced Glutamate and Acetylcarnitine in the Amniotic Fluid of Cytomegalovirus-Infected Pregnant Women.Journal of proteome research · 2026Article
- Circulating tumor cells (CTCs) enumeration and machine-learning based diagnostic biomarkers for breast cancer detection.BMC cancer · 2026Article
- Enhancing genomic prediction of wheat resistance to fusarium head blight through integration of passive resistance traits.Frontiers in plant science · 2026Article
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- Balancing ethics and statistics: machine learning facilitates highly accurate classification of mice according to their trait anxiety with reduced sample sizes.Translational psychiatry · 2025Article
- Comparative Analysis of Machine Learning Approaches for Fetal Movement Detection with Linear Acceleration and Angular Rate Signals.Sensors (Basel, Switzerland) · 2025Article
- Multimodal prediction of the need of clozapine in treatment resistant schizophrenia; a pilot study in first-episode psychosis.Biomarkers in neuropsychiatry · 2024Article
- Developing a prediction model for cognitive impairment in older adults following critical illness.BMC geriatrics · 2024Article
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- Proteomic Analysis Identifies Dysregulated Proteins in Albuminuria: A South African Pilot Study.Biology · 2024Article
- Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.Journal of neurology · 2024Article
- Assessing and Addressing Model Trustworthiness Trade-offs in Trauma Triage.International journal of artificial intelligence tools : architectures, languages, algorithms · 2024Article
- Artificial intelligence in triage of COVID-19 patients.Frontiers in artificial intelligence · 2024Article
- In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin.Functional composite materials · 2024Article
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Abstract
Cross-validation (CV) is a resampling approach to evaluate machine learning models when sample size is limited. The number of all possible combinations of folds for the training data, known as CV rounds, are often very small in leave-one-out CV. Alternatively, Monte Carlo cross-validation (MCCV) can be performed with a flexible number of simulations when computational resources are feasible for a study with limited sample size. We conduct extensive simulation studies to compare accuracy between MCCV and CV with the same number of simulations for a study with binary outcome (e.g., disease progression or not). Accuracy of MCCV is generally higher than CV although the gain is small. They have similar performance when sample size is large. Meanwhile, MCCV is going to provide reliable performance metrics as the number of simulations increases. Two real examples are used to illustrate the comparison between MCCV and CV.
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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.