Hi,

I am using glmnet for my data and have questions regarding cv.glmnet:

1. Is the 10 fold CV stratified cross validation for binary classification
problem?

2. I am doing binary classification (family = "binomial"), the plot from
cv.glmnet gives the average auc as well as the error bar with different
lambda. The bottom of the x is lambda and the top of x axis is how many
variables are left. Since it is 10 fold CV, presumably you will have
slightly different number of nonzeros for each fold run, is the number
shown the average number of nonzeros of the 10 folds? If not, does it mean
it's not an honest cross validation?

Thanks,

-Jack

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