kereszvalidáció

Keresztvalidáció, often translated as cross-validation, is a resampling technique used in statistical learning to evaluate and compare machine learning models. It is a crucial method for assessing how well a model will generalize to an independent dataset. The primary goal is to obtain a more reliable estimate of the model's performance than a simple train-test split.

The most common form is k-fold cross-validation. In this approach, the dataset is randomly partitioned into k subsets, or folds, of approximately equal size. The model is then trained k times. In each iteration, one of the folds is held out as the testing set, and the remaining k-1 folds are used for training. The performance metric (e.g., accuracy, mean squared error) is calculated for each iteration. Finally, the average of these performance metrics across all k iterations is taken as the cross-validation performance estimate. This averaging helps to reduce the variance associated with a single train-test split.

Another variation is leave-one-out cross-validation (LOOCV), which is a special case of k-fold cross-validation where k is equal to the number of data points in the dataset. In LOOCV, each data point is used as a testing set exactly once, and the remaining n-1 data points are used for training. While LOOCV provides a nearly unbiased estimate of the model's performance, it can be computationally very expensive for large datasets.

Cross-validation is valuable for model selection and hyperparameter tuning. By comparing the cross-validation performance of different models or different hyperparameter settings, practitioners can choose the configuration that is most likely to perform well on unseen data, thereby avoiding overfitting.