How can knowledge of the PDF of the dependent variable help us during training and testing with 10-fold cross validation? Our professor has not clarified much on the issue and im a bit confused. What will change in my 10-fold method if i know the distribution of my dependent variable? Wouldn't i split the data into 10 subsets, 9 training and 1 testing, doing that 10 times, regardless of distribution?
This is roughly the PDF of my dependent variable but i do not see how finding this will help me during training and testing.


Imagine one of the folds, by coincidence, only got training examples where the house price is less than 450K; would you expect the model in this fold to generalize well to houses well above that price? In general, machine learning is not perfect at extrapolation.
You can employ something called stratification to ensure that the data in the folds follows approximately the original distribution.
