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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 trained 'k' times. Each time, one of the folds is used as the testing set, and the remaining k-1 folds are used for training. This process ensures that every data point is used for both training and testing exactly once across all iterations. After 'k' training rounds, the performance metrics (such as accuracy, precision, recall, or mean squared error) are averaged to provide a more robust estimate of the model's performance than a single train-test split would offer.

Leave-one-out cross-validation (LOOCV) is an extreme case of k-fold cross-validation where k is equal to the number of data points in the dataset. In LOOCV, the model is trained n-1 times, where n is the total number of samples. Each time, a single data point is left out for testing, and the rest are used for training. While LOOCV provides a nearly unbiased estimate of model performance, it can be computationally very expensive for large datasets.

Stratified cross-validation is used for classification tasks, especially when dealing with imbalanced datasets. It ensures that each fold maintains the same proportion of samples for each target class as in the original dataset. This prevents situations where a fold might contain no samples of a particular class, leading to biased performance estimates. Cross-validation helps in understanding the variability of model performance and detecting overfitting or underfitting.