Crossvalidation just applies to assessing model performance. As described in doc , with kfold the average error across the k different partitions will be reported. The model is trained on the complete dataset that you provide to the training function, in this case, "lasso".
Lasso/Elastic Net feature selection with kFold crossvalidation
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I want to understand how Lasso/Elastic Net regression selects the final features when using kFold cross-validation and using the function: [B, stats] = lasso(featData, classData, 'CV', 10) (from the Statistics & ML toolbox).
In my understanding, if the model is trained 10 times on different subsets of the total sample, this may result in different features selected/penalized in every fold. However, the cross-validated model output does not provide any insight on the variability of those features across different folds. Is the best model simply chosen among all folds and applied to the entire training set? Or are features averaged/weighted based on their stability across folds?
There was a related question previously, but nobody ever answered it:
Thanks for your help!