Cross-Validation Visualized: A Narrative Guide to Advanced Methods
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This study delves into the multifaceted nature of cross-validation (CV) techniques in machine learning model evaluation and selection, underscoring the challenge of choosing the most appropriate method due to the plethora of available variants. It aims to clarify and standardize terminology such as sets, groups, folds, and samples pivotal in the CV domain, and introduces an exhaustive compilation of advanced CV methods like leave-one-out, leave-p-out, Monte Carlo, grouped, stratified, and time-split CV within a hold-out CV framework. Through graphical representations, the paper enhances the comprehension of these methodologies, facilitating more informed decision making for practitioners. It further explores the synergy between different CV strategies and advocates for a unified approach to reporting model performance by consolidating essential metrics. The paper culminates in a comprehensive overview of the CV techniques discussed, illustrated with practical examples, offering valuable insights for both novice and experienced researchers in the field.
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For the performance of the model, cross-validation was employed, consisting of partitioning the data into k distinct groups where k-1 constituted the training sets, the remaining group contained the data used to evaluate accuracy, and this process was repeated k times until each group had been used as a validation set.
“For the performance of the model, cross-validation was employed (Allgaier & Pryss, 2024), which consisted of partitioning the data into k distinct groups where k -1 constituted the training sets.The remaining group contained the data used to evaluate the accuracy; this process was repeated k times until each group had been used as a validation set.”
From DISCOURSE ANALYSIS FOR THE DEVELOPMENT OF A CYBERGROOMING DETECTION MODEL ON ROBLOX (2026) · cites this paper as Allgaier & Pryss, 2024
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Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing
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Use of Binary Classification in Non-Invasive Load Monitoring
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- Authors
- Allgaier, J., & Pryss, R.
- Affiliations
- University of Würzburg (DE)
- Corresponding author
- Allgaier, Johannes
- Venue
- Machine Learning and Knowledge Extraction
- Publisher
- MDPI AG
- Published
- 2024-06-20
- Volume
- 6
- Issue
- 2
- Pages
- 1378-1388
- ISSN
- 2504-4990 (ISSN-L)
- OpenAlex
- W4399882764
- Cited by
- 176
- References
- 17
- Citations by year
- 2026: 80 · 2025: 81 · 2024: 15
- FWCI
- 25.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Machine Learning and Knowledge Extraction (publishedVersion, cc-by)
- Online Publication Service of Würzburg University (Würzburg University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- University of Würzburg
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- cross-validation, model evaluation, model selection, leave-one-out cross-validation, Monte Carlo cross-validation, stratified cross-validation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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