Keresztvalidálás
Keresztvalidálás, known in English as cross-validation, is a resampling technique used to evaluate machine learning models on a limited data sample. It provides an estimate of how well the model will generalize to an independent dataset. The core idea is to split the available data into multiple subsets.
In the most common form, k-fold cross-validation, the data is randomly divided into k equal-sized folds. The
Another variant is leave-one-out cross-validation (LOOCV), which is an extreme case of k-fold cross-validation where k
Stratified cross-validation is used for classification tasks to ensure that each fold preserves the percentage of