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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

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  • $\begingroup$ To answer my question, I think I'm missing sleep. Is the correct answer the usual standardization as I did it per each fold? $\endgroup$ – qeddot Apr 16 '20 at 19:39
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You have to normalize at each loop/fold your test set with the SAME normalization you applied to your training set, so something like this:

normalization = StandardScaler().fit(X_train)

X_train = normalization.transform(X_train)
X_test  = normalization.transform(X_test)

If you don't apply the same normalization to the test set then you will have fundamentally modified your test data.

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