ML0006 Cross-Validation

Cross-validation is a statistical method used to evaluate the performance and generalizability of a model.

Common cross-validation techniques are listed below:

1. k-Fold Cross-Validation: The data is divided into k equal parts (folds). The model is trained k times, each time using k – 1 folds for training and the remaining fold for validation. The final performance is the average over all k runs.
2. Leave-One-Out Cross-Validation (LOOCV): A special case of k-fold where k equals the number of data points. Each data point is used once as the validation set, while the rest serve as training data.
3. Stratified k-Fold: Similar to k-fold, but each fold preserves the class distribution, which is particularly useful for imbalanced datasets.