Matlab does provide some built-in functions for cross-validation and hyperparameter tuning for machine learning models. It can be challenging to perform downsampling only on the training data and not on the validation data.
One possible solution is to manually split your data into training and validation sets before performing downsampling on the training data. You can use the training set for hyperparameter tuning and then evaluate your final model on the untouched validation set.
You can also consider using custom cross-validation functions in Matlab that allow you to control the splitting of your data. For example, you can use the cvpartition function to create a custom partitioning scheme that only downsamples the training data while leaving the validation data untouched. Then, you can use this custom partitioning scheme in your cross-validation and hyperparameter tuning process.
