parameters: (self, X)
return_statement: return decisions
docstring: Average of the decision functions of the base classifiers.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape = [n_samples, n_features]
              The training input samples. Sparse matrices are accepted only if
              they are supported by the base estimator.
  
          Returns
          -------
          score : array, shape = [n_samples, k]
              The decision function of the input samples. The columns correspond
              to the classes in sorted order, as they appear in the attribute
              ``classes_``. Regression and binary classification are special
              cases with ``k == 1``, otherwise ``k==n_classes``.
docstring_summary: Average of the decision functions of the base classifiers.
docstring_tokens: Average of the decision functions of the base classifiers .
function: def decision_function(self, X):
          """Average of the decision functions of the base classifiers.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape = [n_samples, n_features]
              The training input samples. Sparse matrices are accepted only if
              they are supported by the base estimator.
  
          Returns
          -------
          score : array, shape = [n_samples, k]
              The decision function of the input samples. The columns correspond
              to the classes in sorted order, as they appear in the attribute
              ``classes_``. Regression and binary classification are special
              cases with ``k == 1``, otherwise ``k==n_classes``.
  
          """
          check_is_fitted(self, "classes_")
  
          # Check data
          X = check_array(X, accept_sparse=['csr', 'csc'])
  
          if self.n_features_ != X.shape[1]:
              raise ValueError("Number of features of the model must "
                               "match the input. Model n_features is {1} and "
                               "input n_features is {2} "
                               "".format(self.n_features_, X.shape[1]))
  
          # Parallel loop
          n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators,
                                                               self.n_jobs)
  
          all_decisions = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
              delayed(_parallel_decision_function)(
                  self.estimators_[starts[i]:starts[i + 1]],
                  self.estimators_features_[starts[i]:starts[i + 1]],
                  X)
              for i in range(n_jobs))
  
          # Reduce
          decisions = sum(all_decisions) / self.n_estimators
  
          return decisions
function_tokens: def decision_function ( self , X ) : check_is_fitted ( self , "classes_" ) # Check data X = check_array ( X , accept_sparse = [ 'csr' , 'csc' ] ) if self . n_features_ != X . shape [ 1 ] : raise ValueError ( "Number of features of the model must " "match the input. Model n_features is {1} and " "input n_features is {2} " "" . format ( self . n_features_ , X . shape [ 1 ] ) ) # Parallel loop n_jobs , n_estimators , starts = _partition_estimators ( self . n_estimators , self . n_jobs ) all_decisions = Parallel ( n_jobs = n_jobs , verbose = self . verbose ) ( delayed ( _parallel_decision_function ) ( self . estimators_ [ starts [ i ] : starts [ i + 1 ] ] , self . estimators_features_ [ starts [ i ] : starts [ i + 1 ] ] , X ) for i in range ( n_jobs ) ) # Reduce decisions = sum ( all_decisions ) / self . n_estimators return decisions