Statements (18)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
cross-validation method
|
| gptkbp:advantage |
computationally expensive for large datasets
high variance in estimate maximizes training data usage |
| gptkbp:alternativeName |
leave-one-out CV
|
| gptkbp:describes |
Each observation in the dataset is used once as a validation set while the remaining observations form the training set.
|
| gptkbp:folds |
number of data points in the dataset
|
| gptkbp:fullName |
Leave-One-Out Cross-Validation
|
| https://www.w3.org/2000/01/rdf-schema#label |
LOO-CV
|
| gptkbp:method |
resampling method
|
| gptkbp:output |
average error across all folds
|
| gptkbp:purpose |
model selection
model evaluation |
| gptkbp:relatedTo |
k-fold cross-validation
|
| gptkbp:usedIn |
gptkb:machine_learning
statistics |
| gptkbp:bfsParent |
gptkb:Deviance_Information_Criterion
|
| gptkbp:bfsLayer |
7
|