Saving & reloading a pipeline? #32652

DrBwts · 2025-11-05T14:27:49Z

DrBwts
Nov 5, 2025

I'm comparing a number of classifiers & want to save the models. For instance,

pipe = make_pipeline(StandardScaler(), GaussianNB())
pipe.fit(X_train, y_train)

with open('my_model', 'wb') as file:
    pickle.dump(pipe, file)

My question is, when I come to use those models, will the pipe line be part of the loaded model or with the pickle file just contain the model parameters & will have to apply the StandardScaler() function to any input data?

Mohataseem89 · 2025-12-12T15:53:29Z

Mohataseem89
Dec 12, 2025

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')

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Mohataseem89 Dec 12, 2025

DrBwts
Nov 5, 2025

Mohataseem89
Dec 12, 2025