# Given a dataset, how to test your model against the test set if you used StratifiedKFold and standardized train and validation sets per fold?

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?

Thank you

• To answer my question, I think I'm missing sleep. Is the correct answer the usual standardization as I did it per each fold? – qeddot Apr 16 '20 at 19:39

normalization = StandardScaler().fit(X_train)