ristivalidointiin

Ristivalidointiin, known in English as cross-validation, is a fundamental technique in machine learning and statistical modeling used to assess how the results from a statistical analysis or model will generalize to an independent dataset. Its primary purpose is to provide a more reliable estimate of model performance than a single train-test split.

The core idea of cross-validation involves dividing the available data into several subsets or "folds." The model is then trained multiple times. In each iteration, one fold is held out as the validation set, and the remaining folds are used for training the model. This process is repeated until every fold has served as a validation set exactly once. The performance metrics calculated on each validation fold are then averaged to obtain an overall estimate of the model's predictive accuracy.

There are several common types of cross-validation, including k-fold cross-validation, stratified k-fold cross-validation (which ensures that each fold has approximately the same percentage of samples of each target class as the complete set), and leave-one-out cross-validation (a special case of k-fold where k equals the number of data points).

Cross-validation is crucial for preventing overfitting, a common problem where a model performs exceptionally well on the training data but poorly on unseen data. By evaluating the model on data it has not been trained on during the process, cross-validation helps in selecting the best model or tuning its hyperparameters to achieve better generalization. It is a standard practice in model evaluation and selection.