Tag: k-fold

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k-fold cross validation

In machine learning, k-fold cross validation is a type of cross validation in which the dataset is split into k subsets and the validation process is repeated k times. Each time k-1 subsets are used for training the ML model, while one (1) subset is used for testing/validation. The final validation generalization capability/performance of the ... Read more

stratified cross validation

Stratified cross validation is a data validation technique used when splitting the ML dataset into k subsets, of which k-1 subsets are used as training subsets (folds) and one (1) is used as the test subset (fold). This process is repeated k times. Stratified cross validation uses stratified sampling in the dataset, in order to ... Read more