class_docstr: Regressor wrapper of the StructureModel.
  Implements the sklearn .fit and .predict interface.
  
  Example:
  ::
      >>> from causalnex.sklearn import DAGRegressor
      >>>
      >>> reg = DAGRegressor(threshold=0.1)
      >>> reg.fit(X_train, y_train)
      >>>
      >>> y_preds = reg.predict(X_test)
      >>> type(y_preds)
      np.ndarray
      >>>
      >>> type(reg.feature_importances_)
      np.ndarray
  ::
  
  Attributes:
      feature_importances_ (np.ndarray): An array of edge weights corresponding
      positionally to the feature X.
  
      coef_ (np.ndarray): An array of edge weights corresponding
      positionally to the feature X.
  
      intercept_ (float): The target node bias value.
docstr: Fits the sm model using the concat of X and y.
  
  Raises:
      NotImplementedError: If unsupported _target_dist_type provided.
  
  Returns:
      Instance of DAGRegressor.
docstr_tok: ["Fits","the","sm","model","using","the","concat","of","X","and","y",".","Raises",":","NotImplementedError",":","If","unsupported","_target_dist_type","provided",".","Returns",":","Instance","of","DAGRegressor","."]
code: def fit(
      self,
      X: Union[pd.DataFrame, np.ndarray],
      y: Union[pd.Series, np.ndarray],
  ) -> "DAGRegressor":
      """
      Fits the sm model using the concat of X and y.
  
      Raises:
          NotImplementedError: If unsupported _target_dist_type provided.
  
      Returns:
          Instance of DAGRegressor.
      """
  
      # store the protected attr _target_dist_type
      if self.target_dist_type is None:
          self.target_dist_type = "cont"
  
      # fit the NOTEARS model
      super().fit(X, y)
      return self
  
file_path: causalnex/causalnex/structure/pytorch/sklearn/reg.py
imports: from typing import Union
  import numpy
  import pandas
  from sklearn.base import RegressorMixin
  from causalnex.structure.pytorch.sklearn._base import DAGBase
label_desc: Use image node_id 1 for calling the DAGRegressor obj's underlying member method code with example usage: obj.fit(X, y) and returns: self