parameters: (self, data, prediction_type='VirtEnsembles', ntree_end=0, virtual_ensembles_count=10, thread_count=-1, verbose=None)
return_statement: return self._virtual_ensembles_predict(data, prediction_type, ntree_end, virtual_ensembles_count, thread_count, verbose, 'virtual_ensembles_predict')
docstring: Predict with data.
  
          Parameters
          ----------
          data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series
                  or catboost.FeaturesData
              Data to apply model on.
              If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted
              as a list of features for a single object.
  
          prediction_type : string, optional (default='RawFormulaVal')
              Can be:
              - 'VirtEnsembles': return V (virtual_ensembles_count) predictions.
                  k-th virtEnsemle consists of trees [0, T/2] + [T/2 + T/(2V) * k, T/2 + T/(2V) * (k + 1)]  * constant.
              - 'TotalUncertainty': see returned predictions format in 'Returns' part
  
          ntree_end: int, optional (default=0)
              Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing).
              If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_.
  
          virtual_ensembles_count: int, optional (default=10)
              virtual ensembles count for 'TotalUncertainty' and 'VirtEnsembles' prediction types.
  
          thread_count : int (default=-1)
              The number of threads to use when applying the model.
              Allows you to optimize the speed of execution. This parameter doesn't affect results.
              If -1, then the number of threads is set to the number of CPU cores.
  
          verbose : bool, optional (default=False)
              If True, writes the evaluation metric measured set to stderr.
  
          Returns
          -------
          prediction :
              (with V as virtual_ensembles_count and T as trees count,
              k-th virtEnsemle consists of trees [0, T/2] + [T/2 + T/(2V) * k, T/2 + T/(2V) * (k + 1)]  * constant)
              If data is for a single object, return 1-dimensional array of predictions with size depends on prediction type,
              otherwise return 2-dimensional numpy.ndarray with shape (number_of_objects x size depends on prediction type);
              Returned predictions depends on prediction type:
              If loss-function was RMSEWithUncertainty:
                  - 'VirtEnsembles': [mean0, var0, mean1, var1, ..., vark-1].
                  - 'TotalUncertainty': [mean_predict, KnowledgeUnc, DataUnc].
              otherwise for regression:
                  - 'VirtEnsembles':  [mean0, mean1, ...].
                  - 'TotalUncertainty': [mean_predicts, KnowledgeUnc].
              otherwise for binary classification:
                  - 'VirtEnsembles':  [ApproxRawFormulaVal0, ApproxRawFormulaVal1, ..., ApproxRawFormulaValk-1].
                  - 'TotalUncertainty':  [DataUnc, TotalUnc].
docstring_summary: Predict with data.
docstring_tokens: Predict with data .
function: def virtual_ensembles_predict(self, data, prediction_type='VirtEnsembles', ntree_end=0, virtual_ensembles_count=10, thread_count=-1, verbose=None):
          """
          Predict with data.
  
          Parameters
          ----------
          data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series
                  or catboost.FeaturesData
              Data to apply model on.
              If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted
              as a list of features for a single object.
  
          prediction_type : string, optional (default='RawFormulaVal')
              Can be:
              - 'VirtEnsembles': return V (virtual_ensembles_count) predictions.
                  k-th virtEnsemle consists of trees [0, T/2] + [T/2 + T/(2V) * k, T/2 + T/(2V) * (k + 1)]  * constant.
              - 'TotalUncertainty': see returned predictions format in 'Returns' part
  
          ntree_end: int, optional (default=0)
              Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing).
              If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_.
  
          virtual_ensembles_count: int, optional (default=10)
              virtual ensembles count for 'TotalUncertainty' and 'VirtEnsembles' prediction types.
  
          thread_count : int (default=-1)
              The number of threads to use when applying the model.
              Allows you to optimize the speed of execution. This parameter doesn't affect results.
              If -1, then the number of threads is set to the number of CPU cores.
  
          verbose : bool, optional (default=False)
              If True, writes the evaluation metric measured set to stderr.
  
          Returns
          -------
          prediction :
              (with V as virtual_ensembles_count and T as trees count,
              k-th virtEnsemle consists of trees [0, T/2] + [T/2 + T/(2V) * k, T/2 + T/(2V) * (k + 1)]  * constant)
              If data is for a single object, return 1-dimensional array of predictions with size depends on prediction type,
              otherwise return 2-dimensional numpy.ndarray with shape (number_of_objects x size depends on prediction type);
              Returned predictions depends on prediction type:
              If loss-function was RMSEWithUncertainty:
                  - 'VirtEnsembles': [mean0, var0, mean1, var1, ..., vark-1].
                  - 'TotalUncertainty': [mean_predict, KnowledgeUnc, DataUnc].
              otherwise for regression:
                  - 'VirtEnsembles':  [mean0, mean1, ...].
                  - 'TotalUncertainty': [mean_predicts, KnowledgeUnc].
              otherwise for binary classification:
                  - 'VirtEnsembles':  [ApproxRawFormulaVal0, ApproxRawFormulaVal1, ..., ApproxRawFormulaValk-1].
                  - 'TotalUncertainty':  [DataUnc, TotalUnc].
          """
          return self._virtual_ensembles_predict(data, prediction_type, ntree_end, virtual_ensembles_count, thread_count, verbose, 'virtual_ensembles_predict')
function_tokens: def virtual_ensembles_predict ( self , data , prediction_type = 'VirtEnsembles' , ntree_end = 0 , virtual_ensembles_count = 10 , thread_count = - 1 , verbose = None ) : return self . _virtual_ensembles_predict ( data , prediction_type , ntree_end , virtual_ensembles_count , thread_count , verbose , 'virtual_ensembles_predict' )