How to use GridSearchCV output for a scikit prediction?


gs.predict(X_test) is equivalent to gs.best_estimator_.predict(X_test). Using either, X_test will be passed through your entire pipeline and it will return the predictions.

gs.best_estimator_.named_steps['clf'].predict(), however is only the last phase of the pipeline. To use it, the feature selection step must already have been performed. This would only work if you have previously run your data through gs.best_estimator_.named_steps['fs'].transform()

Three equivalent methods for generating predictions are shown below:

Using gs directly.

pred = gs.predict(X_test)

Using best_estimator_.

pred = gs.best_estimator_.predict(X_test)

Calling each step in the pipeline individual.

X_test_fs = gs.best_estimator_.named_steps['fs'].transform(X_test)
pred = gs.best_estimator_.named_steps['clf'].predict(X_test_fs)

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Updated on June 04, 2022


  • user308827
    user308827 12 months

    In the following code:

    # Load dataset
    iris = datasets.load_iris()
    X, y =,
    rf_feature_imp = RandomForestClassifier(100)
    feat_selection = SelectFromModel(rf_feature_imp, threshold=0.5)
    clf = RandomForestClassifier(5000)
    model = Pipeline([
              ('fs', feat_selection), 
              ('clf', clf), 
     params = {
        'fs__threshold': [0.5, 0.3, 0.7],
        'fs__estimator__max_features': ['auto', 'sqrt', 'log2'],
        'clf__max_features': ['auto', 'sqrt', 'log2'],
     gs = GridSearchCV(model, params, ...),y)

    What should be used for a prediction?

    • gs?
    • gs.best_estimator_? or
    • gs.best_estimator_.named_steps['clf']?

    What is the difference between these 3?

  • rajesh
    rajesh almost 2 years
    Thank you very much! Is there an offcial doc saying the same?