1) I was studying about
cross-validation and have a bit of confusion here. I understand about the
k-fold technique, where if you have 100 data and do
10 folds validation, you use the
n=10 data for training and another
n=90 for calculating the error (or maybe the other way round?
However, when you do leave on out, you are basically training on
n=99 data, and calculating error on the
n=1 data? how do this work? because you can't basically fit on one data? Or am I getting something wrong here?
2) Secondly, isn't fitting data with a higher degree of polynomial always yield less error?