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
code: def __init__(self, strategy="mean", constant=None, quantile=None):
      self.strategy = strategy
      self.constant = constant
      self.quantile = quantile
      self.sklearn_dummy_regressor = SklearnDummyRegressor(
          strategy=strategy, constant=constant, quantile=quantile
      )
      super().__init__()
  
imports: import numpy
  from sklearn.dummy import DummyRegressor
  from sktime.regression.base import BaseRegressor
label_desc: Use image node_id 1 to create a new DummyRegressor object from inherited base classes: BaseRegressor with example: obj = DummyRegressor(strategy, constant, quantile)