k-fold cross-validation

K-fold cross-validation is a method used to evaluate how well a predictive model performs. It works by dividing the dataset into a set number of equal parts, or "folds." The model is trained on most of these parts and tested on the remaining one. This process repeats so each part is used once for testing. The results are then averaged to give a sense of the model’s overall accuracy. This approach helps ensure the model works well across different data samples and reduces the chance of overfitting to a specific subset.