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Cross-validation splits the data into k folds, trains on k minus one and tests on the last, rotating so every sample is tested once; stratified folds keep the class balance the same in each fold.
Cross-validation is a family of model validation techniques for assessing how a statistical analysis will generalise to independent data, using resampling and sample splitting (Wikipedia); stratifying the folds (Wikipedia on stratified sampling) keeps rare classes represented. It estimates performance but is not a substitute for an external cohort, and every preprocessing step (scaling, PCA, feature selection) must be fitted inside the training fold or the estimate leaks. Reporting the seed makes the folds reproducible.
Shares Data leakage in model evaluation, External validation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Train, validation and test split discipline, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Bootstrap resampling, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Data leakage in model evaluation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Bootstrap resampling, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Bootstrap resampling, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares External validation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares External validation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.