ظهور الخطأ التالي IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices  أثناء بناء نموذج BaggingClassifier 

قمت ببناء نموذج  BaggingClassifier  لكن يظهر لي الخطأ التالي:

from sklearn.ensemble import BaggingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.datasets import load_breast_cancer
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.svm import SVR
Data = load_breast_cancer()
X = Data.data
y = Data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=44, shuffle =True)
clf = BaggingClassifier(base_estimator=SVR(),n_estimators=150, random_state=444)
clf.fit(X_train, y_train)
print('BaggingClassifier Train Score is : ' , clf.score(X_train, y_train)) 
print('BaggingClassifier Test Score is : ' , clf.score(X_test, y_test)) 
c = confusion_matrix(y_test, clf.predict(X_test))
print('Confusion Matrix is : \n', c)
sns.heatmap(c, center = True)
plt.show()
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-45-1d3261b5c2fc> in <module>
     12 clf = BaggingClassifier(base_estimator=SVR(),n_estimators=150, random_state=444)
     13 clf.fit(X_train, y_train)
---> 14 print('BaggingClassifier Train Score is : ' , clf.score(X_train, y_train))
     15 print('BaggingClassifier Test Score is : ' , clf.score(X_test, y_test))
     16 c = confusion_matrix(y_test, clf.predict(X_test))

~\anaconda3\lib\site-packages\sklearn\base.py in score(self, X, y, sample_weight)
    367         """
    368         from .metrics import accuracy_score
--> 369         return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
    370 
    371 

~\anaconda3\lib\site-packages\sklearn\ensemble\_bagging.py in predict(self, X)
    668             The predicted classes.
    669         """
--> 670         predicted_probabilitiy = self.predict_proba(X)
    671         return self.classes_.take((np.argmax(predicted_probabilitiy, axis=1)),
    672                                   axis=0)

~\anaconda3\lib\site-packages\sklearn\ensemble\_bagging.py in predict_proba(self, X)
    718                 X,
    719                 self.n_classes_)
--> 720             for i in range(n_jobs))
    721 
    722         # Reduce

~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
   1002             # remaining jobs.
   1003             self._iterating = False
-> 1004             if self.dispatch_one_batch(iterator):
   1005                 self._iterating = self._original_iterator is not None
   1006 

~\anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
    833                 return False
    834             else:
--> 835                 self._dispatch(tasks)
    836                 return True
    837 

~\anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
    752         with self._lock:
    753             job_idx = len(self._jobs)
--> 754             job = self._backend.apply_async(batch, callback=cb)
    755             # A job can complete so quickly than its callback is
    756             # called before we get here, causing self._jobs to

~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
    207     def apply_async(self, func, callback=None):
    208         """Schedule a func to be run"""
--> 209         result = ImmediateResult(func)
    210         if callback:
    211             callback(result)

~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
    588         # Don't delay the application, to avoid keeping the input
    589         # arguments in memory
--> 590         self.results = batch()
    591 
    592     def get(self):

~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~\anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~\anaconda3\lib\site-packages\sklearn\ensemble\_bagging.py in _parallel_predict_proba(estimators, estimators_features, X, n_classes)
    140 
    141             for i in range(n_samples):
--> 142                 proba[i, predictions[i]] += 1
    143 
    144     return proba

IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

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