What is the role of cross-validation in ML? Name a few techniques.
What is the role of cross-validation in ML? Name a few techniques.
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21-Apr-2025
Updated on 24-Apr-2025
Khushi Singh
24-Apr-2025Machine learning models require cross-validation because it enables performance evaluation of how well models predict new data points. The performance assessment method relies on dataset splitting to create multiple subsets before using subsets for model training while checking the model on unsed subsets. The methodology decreases model overfitting while confirming that model performance does not depend on the particular train-test separation.
Cross-validation achieves its goal through efficient use of available dataset information particularly within small data conditions. Multiple subsets divided from the dataset enable better model performance estimation compared to one isolated train-test split and facilitate both model selection and hyperparameter tuning as well as error estimation.
Common Cross-Validation Techniques:
K-Fold Cross-Validation: The available dataset gets divided evenly into k distinct parts known as folds. After training using k-1 folds the model runs its validation operation against the remaining one. The procedure is executed k times while averaging the performance metrics.
Stratified K-Fold Cross-Validation: The cross-validation method maintains the original class distribution within each division similar to K-Fold method. Cross-validation becomes specifically effective for handling datasets with unequal class distributions.
Leave-One-Out Cross-Validation (LOOCV): The entire dataset gets sequentially used for validation purposes during which the remaining samples remain in the training phase. The algorithm runs a very detailed analysis at a high computational cost.
Repeated K-Fold: The method employs multiple sequential K-Fold cross-validation procedures to create different random splits in order to improve evaluation stability.
Time Series Split: The method applies to sequential temporal data that requires training to occur before any validation process. Random shuffling is avoided in this method because it preserves sequence order.