class_docstr: Pandas Wrapper for DummyRegressor. Unlike DummyRegressor, it uses X for fitting instead of y
  Otherwise, the interface is identical.
  
  This allows the DummyRegressor to be part of a Pipeline which transforms X[value_col] before fitting.
  
  Does not implement BaseForecastEstimator, because it is imported by that class.
  
  Parameters
  ----------
  strategy : str
      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 of shape = [n_outputs]
      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]
      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.
  
  score_func : callable, optional, default=mean_squared_error
      Function to calculate model R2_null_model_score score,
      with signature (actual, predicted).
      `actual`, `predicted` are array-like with the same shape.
      Smaller values are better.
  
  Attributes
  ----------
  coverage : None
      Model coverage.
      Always None, provided for compatibility with
      :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`.
  null_model: None
      Null model.
      Always None, provided for compatibility with
      :func:`~greykite.framework.pipeline.pipeline.forecast_pipeline`.
  model : `~sklearn.dummy.DummyRegressor`
      Trained model
  time_col_ : `str`
      Name of timestamp column
  value_col_ : `str`
      Name of value column
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docstr: Prints input parameters and DummyRegressor model parameters
          
docstr_tok: ["Prints","input","parameters","and","DummyRegressor","model","parameters"]
code: def summary(self):
      """Prints input parameters and DummyRegressor model parameters"""
      log_message(self, LoggingLevelEnum.DEBUG)
      log_message(self.model, LoggingLevelEnum.DEBUG)
  
file_path: greykite/greykite/sklearn/estimator/null_model.py
imports: import pandas
  from sklearn.base import BaseEstimator
  from sklearn.base import RegressorMixin
  from sklearn.dummy import DummyRegressor
  from sklearn.metrics import mean_squared_error
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
label_desc: Use image node_id 4 for calling the DummyEstimator obj's underlying member method code with example usage: obj.summary() without return types