krosszvalidációt

Krosszvalidáció (cross-validation) is a statistical method used to estimate the performance of a machine learning model. It involves partitioning the data into subsets, training the model on some subsets, and validating it on the remaining subsets. This process is repeated multiple times to ensure that the model's performance is consistent and not dependent on a particular data split.

There are several types of cross-validation techniques, including k-fold cross-validation, leave-one-out cross-validation, and stratified k-fold cross-validation. In k-fold cross-validation, the data is divided into k equally sized folds. The model is trained on k-1 folds and validated on the remaining fold. This process is repeated k times, with each fold used as the validation set once. The results are then averaged to provide a more robust estimate of the model's performance.

Leave-one-out cross-validation is a special case of k-fold cross-validation where k is equal to the number of data points. This method is computationally expensive but provides a very accurate estimate of the model's performance.

Stratified k-fold cross-validation is used when the data is imbalanced. It ensures that each fold has the same proportion of classes as the original dataset, which helps in maintaining the balance during the validation process.

Cross-validation is crucial in model selection and hyperparameter tuning. It helps in selecting the best model and hyperparameters by evaluating their performance on multiple data splits. This ensures that the model generalizes well to unseen data and avoids overfitting.