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!
  1 Comment
Tyson on 23 Jul 2018
This is an important thread. We are also looking for clarification on this exact question. We do not find any info about the beta values for the k-folds in the FitInfo, only a single set of beta values for each lambda. Exactly how were these betas determined?

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Answers (1)

Bernhard Suhm
Bernhard Suhm on 22 Apr 2018

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".

Juliana Corlier
Juliana Corlier on 11 May 2018
Thanks for clarifying this! This is very helpful. I have a practical follow up question:
I was looking for these pointers, but I can't seem to find them. In the FitInfo struct I only get coefficients for the 72 different Lambda values (which I also get if I don't run crossvalidation). I would have expected a multidimensional struct/object for different kFolds, but my FitInfo is a 1x1 struct. Any ideas on that? Many thanks!

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