Issue
I am trying to use a stacking classifier along with 3 base learners of random forest, boosting and SVM and 1 meta learner of logistic regression.
However I keep getting this error message.
from sklearn.ensemble import StackingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
rf = RandomForestClassifier(n_estimators=100,random_state=100)
gb = GradientBoostingClassifier(n_estimators=100,random_state=100)
svm = make_pipeline(StandardScaler(), SVC(random_state=100))
estimators = [('RF', rf),
('GB', gb),
('SVM', svm)]
Model = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression)
Model.fit(X_train,y_train).score(X_val,y_val)
But I keep getting this error.
TypeError Traceback (most recent call last)
<ipython-input-47-40186fb4189e> in <module>
----> 1 Model.fit(X_train,y_train).score(X_val,y_val)
~\anaconda3\lib\site-packages\sklearn\ensemble\_stacking.py in fit(self, X, y, sample_weight)
437 self._le = LabelEncoder().fit(y)
438 self.classes_ = self._le.classes_
--> 439 return super().fit(X, self._le.transform(y), sample_weight)
440
441 @if_delegate_has_method(delegate='final_estimator_')
~\anaconda3\lib\site-packages\sklearn\ensemble\_stacking.py in fit(self, X, y, sample_weight)
138 # 'drop' string.
139 names, all_estimators = self._validate_estimators()
--> 140 self._validate_final_estimator()
141
142 stack_method = [self.stack_method] * len(all_estimators)
~\anaconda3\lib\site-packages\sklearn\ensemble\_stacking.py in _validate_final_estimator(self)
406
407 def _validate_final_estimator(self):
--> 408 self._clone_final_estimator(default=LogisticRegression())
409 if not is_classifier(self.final_estimator_):
410 raise ValueError(
~\anaconda3\lib\site-packages\sklearn\ensemble\_stacking.py in _clone_final_estimator(self,
default)
55 def _clone_final_estimator(self, default):
56 if self.final_estimator is not None:
--> 57 self.final_estimator_ = clone(self.final_estimator)
58 else:
59 self.final_estimator_ = clone(default)
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0
~\anaconda3\lib\site-packages\sklearn\base.py in clone(estimator, safe)
62 if isinstance(estimator, type):
63 raise TypeError("Cannot clone object. " +
--> 64 "You should provide an instance of " +
65 "scikit-learn estimator instead of a class.")
66 else:
TypeError: Cannot clone object. You should provide an instance of scikit-learn estimator
instead
of a class.
I am applying this on the titanic Data set to use the power of all the algorithms at my disposal. I have never used stacked classification or regression before and hence this is my first time.
Thanks and Regards
Solution
As @amiola pointed out, you're missing the parenthesis after LogisticRegression
which will create a new instance of that class:
Model = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression)
should be
Model = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression())
^^
Answered By - user17242583
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