# Misclassification for test and training sets

I have a problem where I need to provide the misclassification error for both training- and test-set. I am working with logistic regression, so I have a binomial family for my models. I have two models, one is lasso and the other is ridge. I need to find the misclassification error for training and test for both my lasso and ridge models. I am trying to solve this problem in R. Does anyone have any ideas if there is a formula/function in which this works out?

Also, wouldn't the training error for my models just be 0? As I am training my model with this data, and then predicting it against the same data? Thanks.

I think you can use glmnet package, with options family='binomial' and adjusting ElasticNet coefficient alpha to balance between L1 and L2 regularisations. An example is here.