Will the test data set undergo all the operation in clf?

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1
  • MNE version: e.g. 0.24.0

Hey guys!
I have a naive question in this tutorial:
https://mne.tools/dev/auto_examples/decoding/decoding_time_generalization_conditions.html?highlight=generalizingestimator

specifically in this part:

clf = make_pipeline(

    StandardScaler(),

    LogisticRegression(solver='liblinear')  # liblinear is faster than lbfgs

)

time_gen = GeneralizingEstimator(clf, scoring='roc_auc', n_jobs=None,

                                 verbose=True)

# Fit classifiers on the epochs where the stimulus was presented to the left.

# Note that the experimental condition y indicates auditory or visual

time_gen.fit(X=epochs['Left'].get_data(),

             y=epochs['Left'].events[:, 2] > 2)

scores = time_gen.score(X=epochs['Right'].get_data(),
                        y=epochs['Right'].events[:, 2] > 2)

my question is will the data of the test set epochs['Right'].get_data() will be standardized using StandardScaler() in clf or only the training data epochs['Left'].get_data() set will be standardized? :thinking:

2

Yes test with undergo the same preprocessing

It’s the scikit-learn pipeline API

Alex

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