text: 
  from evalml.objectives import get_objective
  from evalml.pipelines import PipelineBase
  from evalml.problem_types import ProblemTypes
  from evalml.utils import _convert_woodwork_types_wrapper, infer_feature_types
  
  
  class RegressionPipeline(PipelineBase):
      """Pipeline subclass for all regression pipelines."""
      problem_type = ProblemTypes.REGRESSION
  
      def fit(self, X, y):
          """Build a regression model.
  
          Arguments:
              X (ww.DataTable, pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features]
              y (ww.DataColumn, pd.Series, np.ndarray): The target training data of length [n_samples]
  
          Returns:
              self
          """
          X = infer_feature_types(X)
          y = infer_feature_types(y)
          if "numeric" not in y.semantic_tags:
              raise ValueError(f"Regression pipeline can only handle numeric target data")
          y = _convert_woodwork_types_wrapper(y.to_series())
  
          self._fit(X, y)
          return self
  
      def score(self, X, y, objectives):
          """Evaluate model performance on current and additional objectives
  
          Arguments:
              X (ww.DataTable, pd.DataFrame, or np.ndarray): Data of shape [n_samples, n_features]
              y (ww.DataColumn, pd.Series, or np.ndarray): True values of length [n_samples]
              objectives (list): Non-empty list of objectives to score on
  
          Returns:
              dict: Ordered dictionary of objective scores
          """
          objectives = [get_objective(o, return_instance=True) for o in objectives]
          y_predicted = _convert_woodwork_types_wrapper(self.predict(X).to_series())
          return self._score_all_objectives(X, y, y_predicted, y_pred_proba=None, objectives=objectives)
  