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Ristiinvalidointi, known in English as cross-validation, is a resampling technique used to evaluate machine learning models on a limited data sample. It is a crucial step in the model development process to assess how the model will generalize to an independent dataset. The core idea is to split the available data into multiple subsets.
In a typical k-fold cross-validation, the dataset is divided into 'k' equal-sized folds. The model is then
Leave-one-out cross-validation (LOOCV) is an extreme case of k-fold cross-validation where k is equal to the number
Stratified cross-validation is used for classification tasks, especially when dealing with imbalanced datasets. It ensures that