class_docstr: DummyRegressor makes predictions that ignore the input features.
  
  This regressor serves as a simple baseline to compare against other more
  complex regressors.
  The specific behavior of the baseline is selected with the `strategy`
  parameter.
  
  All strategies make predictions that ignore the input feature values passed
  as the `X` argument to `fit` and `predict`. The predictions, however,
  typically depend on values observed in the `y` parameter passed to `fit`.
  
  Function-identical to `sklearn.dummy.DummyRegressor`, which is called
  inside.
  
  Parameters
  ----------
  strategy : {"mean", "median", "quantile", "constant"}, default="mean"
      Strategy to use to generate predictions.
  
      * "mean": always predicts the mean of the training set
      * "median": always predicts the median of the training set
      * "quantile": always predicts a specified quantile of the training set,
      provided with the quantile parameter.
      * "constant": always predicts a constant value that is provided by
      the user.
  
  constant : int or float or array-like of shape (n_outputs,), default=None
      The explicit constant as predicted by the "constant" strategy. This
      parameter is useful only for the "constant" strategy.
  
  quantile : float in [0.0, 1.0], default=None
      The quantile to predict using the "quantile" strategy. A quantile of
      0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the
      maximum.
  
  Examples
  --------
  >>> from sktime.regression.dummy import DummyRegressor
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train")
  >>> X_test, y_test = load_unit_test(split="test")
  >>> regressor = DummyRegressor(strategy="median") # doctest: +SKIP
  >>> regressor.fit(X_train,y_train) # doctest: +SKIP
  DummyRegressor(strategy='median')
  >>> y_pred = regressor.predict(X_test) # doctest: +SKIP
docstr: Perform regression on test vectors X.
  
  Parameters
  ----------
  X : sktime-format pandas dataframe or array-like, shape (n, d)
  
  Returns
  -------
  y : predictions of target values for X, np.ndarray
docstr_tok: ["Perform","regression","on","test","vectors","X.","Parameters","--","--","--","--","--","X",":","sktime-format","pandas","dataframe","or","array-like",",","shape","(","n",",","d",")","Returns","--","--","--","-","y",":","predictions","of","target","values","for","X",",","np.ndarray"]
code: def _predict(self, X) -> np.ndarray:
      """Perform regression on test vectors X.
  
      Parameters
      ----------
      X : sktime-format pandas dataframe or array-like, shape (n, d)
  
      Returns
      -------
      y : predictions of target values for X, np.ndarray
      """
      return self.sklearn_dummy_regressor.predict(np.zeros(X.shape))
  
imports: import numpy
  from sklearn.dummy import DummyRegressor
  from sktime.regression.base import BaseRegressor
label_desc: Use image node_id 3 for calling the DummyRegressor obj's underlying member method code with example usage: obj._predict(X) and returns: self