Yes — when you pickle the Pipeline, the entire pipeline is saved, including all steps (StandardScaler, GaussianNB, etc.) and their fitted parameters.

When you load it back, you can use it directly on new data:

with open('my_model', 'rb') as file: pipe = pickle.load(file)

y_pred = pipe.predict(X_new)

You do not need to manually reapply StandardScaler. The loaded pipeline will automatically apply preprocessing → model prediction, exactly as it did before saving.

Just note: scikit-learn recommends saving models using joblib instead of pickle:

from joblib import dump, load dump(pipe, 'my_model.joblib') pipe = load('my_model.joblib')