I couldn't find an answer to the following issue and I kinda feel stuck...
I have a dataset and I want to split it as follows:
- 90% for train and validation
- 10% for test
Now, I want to use
StratifiedKFold() on the 90%. Let's say I want 10 folds. I do the loop, get the indices, get the training and validation sets, use the
StandardScaler() for each fold separately, train the model on each fold, get the average accuracy.
After I do all of these, how should I use the 10% left for testing, given the fact it wasn't standardized? What am I missing?