V-fold

V-fold, or V-fold cross-validation, is a method used in machine learning to evaluate how well a model performs. The dataset is split into V equal parts, or "folds." The model is trained on V-1 of these folds and tested on the remaining one. This process repeats V times, each time with a different fold as the test set. The results are then averaged to provide a reliable estimate of the model’s accuracy. This helps ensure that the model generalizes well to new, unseen data by reducing overfitting and bias.