kereszvalidációval

Keresztvalidáció, known in English as cross-validation, is a resampling technique used to evaluate machine learning models on a limited data sample. It provides a more robust estimate of how the model will perform on unseen data compared to a single train-test split. The fundamental idea is to divide the dataset into several subsets, or "folds." The model is then trained and tested multiple times, with each fold serving as the testing set once, while the remaining folds are used for training.

The most common form is k-fold cross-validation, where k is the number of folds. For example, in 5-fold cross-validation, the data is split into five equal parts. The model is trained on four parts and validated on the fifth. This process is repeated five times, each time holding out a different fold for validation. The performance metric (e.g., accuracy, precision, recall) is then averaged across all k iterations to obtain a more reliable estimate of the model's generalization ability.

Another variant is leave-one-out cross-validation (LOOCV), which is an extreme case of k-fold cross-validation where k equals the number of data points. Each data point is used as a single test set, and the model is trained on all other data points. While LOOCV provides a nearly unbiased estimate of performance, it can be computationally very expensive for large datasets.

Cross-validation is crucial for preventing overfitting, a scenario where a model learns the training data too well, including its noise and idiosyncrasies, leading to poor performance on new data. By testing the model on different subsets of the data, cross-validation helps assess its ability to generalize. It is an essential tool in the model development and evaluation pipeline for machine learning practitioners.