keresztvalidálással

Keresztvalidálás is a technique used in machine learning and statistics to assess how well a predictive model generalizes to an independent dataset. It is a crucial step in model evaluation, helping to prevent overfitting, where a model performs exceptionally well on the training data but poorly on unseen data.

The core idea of keresztvalidálás involves splitting the available dataset into multiple subsets. Typically, the dataset is divided into k equal-sized folds. The model is then trained k times. In each iteration, one fold is held out as the testing set, and the remaining k-1 folds are used for training. After training, the model's performance is evaluated on the held-out fold. This process is repeated such that each fold serves as the testing set exactly once.

The results from each of the k testing sets are then averaged to obtain a single performance metric. This averaged metric provides a more robust estimate of the model's true predictive performance than a single train-test split. Common keresztvalidálás strategies include k-fold keresztvalidálás, stratified k-fold keresztvalidálás (which ensures that class proportions are maintained in each fold), and leave-one-out keresztvalidálás (a special case where k is equal to the number of data points).

By using multiple training and testing splits, keresztvalidálás helps to ensure that the model's performance is not overly dependent on the specific partitioning of the data. This makes the evaluation more reliable and reduces the risk of selecting a model that is merely a good fit for the particular training set used. It is widely employed in model selection, hyperparameter tuning, and feature selection.