MATLAB Source Code for Cross-Validation Implementation

This MATLAB source code provides a practical implementation of cross-validation with clear, readable syntax that facilitates easy modification and adaptation. The implementation employs an efficient k-fold cross-validation algorithm that partitions the dataset into k subsets, iteratively using k-1 folds for training and the remaining fold for validation to ensure robust model generalization and accuracy assessment. The code structure includes key functions for data splitting, model training iteration, and performance evaluation metrics calculation. Through studying this implementation, you'll gain deeper insights into cross-validation mechanisms and their application in machine learning pipeline development. The modular design allows for straightforward integration with various machine learning models and enables customization of validation parameters such as fold count and evaluation metrics. Users can easily extend this foundation to implement stratified cross-validation, nested cross-validation, or adapt it for specific research requirements and complex model validation scenarios.