TypeError                                 Traceback (most recent call last)
<ipython-input-16-6d428d99dd54> in <module>()
----> 1 cross_validate(clf, x_train, y_train, scoring='accuracy', return_train_score = True)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score)
    204             fit_params, return_train_score=return_train_score,
    205             return_times=True)
--> 206         for train, test in cv.split(X, y, groups))
    207
    208     if return_train_score:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
    777             # was dispatched. In particular this covers the edge
    778             # case of Parallel used with an exhausted iterator.
--> 779             while self.dispatch_one_batch(iterator):
    780                 self._iterating = True
    781             else:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
    623                 return False
    624             else:
--> 625                 self._dispatch(tasks)
    626                 return True
    627

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
    586         dispatch_timestamp = time.time()
    587         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 588         job = self._backend.apply_async(batch, callback=cb)
    589         self._jobs.append(job)
    590

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
    109     def apply_async(self, func, callback=None):
    110         """Schedule a func to be run"""
--> 111         result = ImmediateResult(func)
    112         if callback:
    113             callback(result)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
    330         # Don't delay the application, to avoid keeping the input
    331         # arguments in memory
--> 332         self.results = batch()
    333
    334     def get(self):

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
    129
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
    132
    133     def __len__(self):

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
    129
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
    132
    133     def __len__(self):

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
    486         fit_time = time.time() - start_time
    487         # _score will return dict if is_multimetric is True
--> 488         test_scores = _score(estimator, X_test, y_test, scorer, is_multimetric)
    489         score_time = time.time() - start_time - fit_time
    490         if return_train_score:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _score(estimator, X_test, y_test, scorer, is_multimetric)
    521     """
    522     if is_multimetric:
--> 523         return _multimetric_score(estimator, X_test, y_test, scorer)
    524     else:
    525         if y_test is None:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _multimetric_score(estimator, X_test, y_test, scorers)
    551             score = scorer(estimator, X_test)
    552         else:
--> 553             score = scorer(estimator, X_test, y_test)
    554
    555         if hasattr(score, 'item'):

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\scorer.py in __call__(self, estimator, X, y_true, sample_weight)
     99         super(_PredictScorer, self).__call__(estimator, X, y_true,
    100                                              sample_weight=sample_weight)
--> 101         y_pred = estimator.predict(X)
    102         if sample_weight is not None:
    103             return self._sign * self._score_func(y_true, y_pred,

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\voting_classifier.py in predict(self, X)
    223                 lambda x: np.argmax(
    224                     np.bincount(x, weights=self._weights_not_none)),
--> 225                 axis=1, arr=predictions)
    226
    227         maj = self.le_.inverse_transform(maj)

C:\ProgramData\Anaconda3\lib\site-packages\numpy\lib\shape_base.py in apply_along_axis(func1d, axis, arr, *args, **kwargs)
    130     except StopIteration:
    131         raise ValueError('Cannot apply_along_axis when any iteration dimensions are 0')
--> 132     res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
    133
    134     # build a buffer for storing evaluations of func1d.

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\voting_classifier.py in <lambda>(x)
    222             maj = np.apply_along_axis(
    223                 lambda x: np.argmax(
--> 224                     np.bincount(x, weights=self._weights_not_none)),
    225                 axis=1, arr=predictions)
    226

TypeError: Cannot cast array data from dtype('float64') to dtype('int64') according to the rule 'safe'