parameters: (self, X)
return_statement: return self._get_median_predict(X, len(self.estimators_))
docstring: Predict regression value for X.
  
          The predicted regression value of an input sample is computed
          as the weighted median prediction of the classifiers in the ensemble.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape = [n_samples, n_features]
              The training input samples. Sparse matrix can be CSC, CSR, COO,
              DOK, or LIL. DOK and LIL are converted to CSR.
  
          Returns
          -------
          y : array of shape = [n_samples]
              The predicted regression values.
docstring_summary: Predict regression value for X.
docstring_tokens: Predict regression value for X .
function: def predict(self, X):
          """Predict regression value for X.
  
          The predicted regression value of an input sample is computed
          as the weighted median prediction of the classifiers in the ensemble.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape = [n_samples, n_features]
              The training input samples. Sparse matrix can be CSC, CSR, COO,
              DOK, or LIL. DOK and LIL are converted to CSR.
  
          Returns
          -------
          y : array of shape = [n_samples]
              The predicted regression values.
          """
          check_is_fitted(self, "estimator_weights_")
          X = self._validate_X_predict(X)
  
          return self._get_median_predict(X, len(self.estimators_))
function_tokens: def predict ( self , X ) : check_is_fitted ( self , "estimator_weights_" ) X = self . _validate_X_predict ( X ) return self . _get_median_predict ( X , len ( self . estimators_ ) )