why is cross validation important which of the following is the best way to perform cross validation on d? why does cross validation always produce a better model how to perform cross validation if there is a majority class what is stratified cross validation does cross validation work if there is no majority in the training example why is cross validation a step up or a down why does cross validation give a 'good' prediction why use cross validation on d dataset what is the difference between leave one out and stratified cross validation why do i need to perform cross validation what is cross validation how to do a stratified cross validation analysis why is cross validation used in a naive algorithm when performing cross validation on d the same as when using half of the examples for training and testing what is the correct answer to cross validation? what kind of training algorithm is l if the dataset is naive how many examples can be used to test? l vs 10fold test what is the difference between l and cross validation