Description
Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. When a specific value for k is chosen, it may be used in place of k in the reference to the model, such as k=10 becoming 10-fold cross-validation
Technical Details
| Framework | MITRE D3FEND |
| Ontology URI | d3f:K-FoldCross-Validation |
| Local Identifier | K-FoldCross-Validation |
| Publication Status | Exists in ontology only |