Splitting data into trainning/testing datasets in MATLAB?

Question

Upon some research I found two functions in MATLAB to do the task:

• cvpartition function in the Statistics Toolbox
• crossvalind function in the Bioinformatics Toolbox

Now I've used the cvpartition to create n-fold cross validation subsets before, along with the Dataset/Nominal classes from the Statistics toolbox. So I'm just wondering what are the differences between the two and the pros/cons of each?

Answer 1

It might be a good idea to post your question on Matlab Central

Answer 2

They look to be pretty similar based on the official docs of cvpartition and crossvalind, but crossvalind looks slightly more flexible (it allows for leave M out for arbitrary M, whereas cvpartition only allows for leave 1 out).

Answer 3

They look to be pretty similar based on the official docs of cvpartition and crossvalind, but crossvalind looks slightly more flexible (it allows for leave M out for arbitrary M, whereas cvpartition only allows for leave 1 out).

isnt it true that you can always simulate a leave-M-out using kfold cross validation with an appropriate k value (split data into k fold, test on one, train on all others, and do this for all folds and take average) since leave-one-out is a special case of kfold where k=number of observations.