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I have a pipeline and like this that I pass to GridSearchCV, how can I access the attributes of the scaler from clf?

pipe = Pipeline([
        ('scale', MinMaxScaler()),
        ('clf', tree.DecisionTreeClassifier(presort=True))
    ])

pipe_param_grid = {
            'clf__min_samples_split': [2, 4, 6],
            'clf__random_state': [38, 40, 42, 44],
            'clf__max_depth': [4],
            'clf__min_samples_leaf': [2, 4],
          }

clf = GridSearchCV(estimator=pipe,param_grid=pipe_param_grid)
  • Python: 2.7.10
  • scikit-learn: 0.18.1
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1 Answer 1

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Figured it out. I can access the step attributes like this:

clf.best_estimator_.named_steps['scale'].scale_
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