parameters: (self, X)
return_statement: return y_hat
docstring: Predict regression target for X.
  
          The predicted regression target of an input sample is computed as the
          mean predicted regression targets of the trees in the forest.
  
          Parameters
          ----------
          X : array-like or sparse matrix of shape = [n_samples, n_features]
              The input samples. Internally, its dtype will be converted to
              ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csr_matrix``.
  
          Returns
          -------
          y : array of shape = [n_samples] or [n_samples, n_outputs]
              The predicted values.
docstring_summary: Predict regression target for X.
docstring_tokens: Predict regression target for X .
function: def predict(self, X):
          """Predict regression target for X.
  
          The predicted regression target of an input sample is computed as the
          mean predicted regression targets of the trees in the forest.
  
          Parameters
          ----------
          X : array-like or sparse matrix of shape = [n_samples, n_features]
              The input samples. Internally, its dtype will be converted to
              ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csr_matrix``.
  
          Returns
          -------
          y : array of shape = [n_samples] or [n_samples, n_outputs]
              The predicted values.
          """
          # Check data
          X = self._validate_X_predict(X)
  
          # Assign chunk of trees to jobs
          n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
  
          # Parallel loop
          all_y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose,
                               backend="threading")(
              delayed(parallel_helper)(e, 'predict', X, check_input=False)
              for e in self.estimators_)
  
          # Reduce
          y_hat = sum(all_y_hat) / len(self.estimators_)
  
          return y_hat
function_tokens: def predict ( self , X ) : # Check data X = self . _validate_X_predict ( X ) # Assign chunk of trees to jobs n_jobs , _ , _ = _partition_estimators ( self . n_estimators , self . n_jobs ) # Parallel loop all_y_hat = Parallel ( n_jobs = n_jobs , verbose = self . verbose , backend = "threading" ) ( delayed ( parallel_helper ) ( e , 'predict' , X , check_input = False ) for e in self . estimators_ ) # Reduce y_hat = sum ( all_y_hat ) / len ( self . estimators_ ) return y_hat