I am working with scikit learn and GridSearch in order to find the best parameters in my classifiers.
I have a map of different hyperparameters and I want to print out GridSearch results, but I do not understand one thing - what is the difference between
As I understand, GridSearch performs cross-validation in order to find the best classifier, but how do these 2 params differ from one another? I always thought that cross-validation gives only one mean, which is a mean of the performance from trained models using N subsets of given data. For example, if I perform a cross-validation with X subsets, I will have X different accuracy scores and then I will have only one mean value.
So, how can I interpret these 2 parameters and what is the difference between them?