Cross-Validation Modeling

Cross-validation divides available data into multiple subsets for training and validation. The model is trained on some subsets and evaluated on the remaining subset, with the process repeated across different splits. A common approach is k-fold cross-validation, where the dataset is divided into k groups and each group is used for validation once.

Evaluating a model on only one data split can produce misleading results. Cross-validation provides a more reliable estimate of model performance by testing it across multiple subsets. It also helps identify overfitting, compare different models, and guide hyperparameter selection.