I have different image datasets, most of them are sorted by class, others are already mixed.
For each of these data sets, I would like to train one SVM (in Python with Scikit-Learn), whereby in each case the hyperparameters are previously optimized using GridSearchCV.
Some of the ordered data sets still need to be split into training and test data, which would then be in mixed form after using
train_test_split and, to my understanding, would not have to be mixed again before using
Other ordered data sets are already split into training and test data, so I would directly apply
GridSearchCV. Now I have found that
StratifiedKFold does not mix the data before splitting into batches, if I understood correctly. In those cases, I should mix the data before using GridSearchCV, right?
If I am mistaken in my assumptions, I would be grateful for any hint.