K-fold

K-fold is a cross-validation technique used in machine learning to evaluate the performance of a model. It involves dividing the dataset into K equal subsets or folds, where K-1 folds are used for training the model and the remaining fold is used for testing. This process is repeated K times, with each fold being used as the test set once. The results are then averaged to give an overall performance metric for the model. K-fold is useful for assessing the variance of a model and reducing the risk of overfitting. It is also helpful in cases where the dataset is small and there is a need to make the most of the available data.

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K-fold

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