class_docstr: Regressor wrapped for the Rocket transformer using RidgeCV regressor.
  
  This regressor simply transforms the input data using the Rocket [1]_
  transformer and builds a RidgeCV estimator using the transformed data.
  
  Shorthand for the pipeline
  `rocket * StandardScaler(with_mean=False) * RidgeCV(alphas)`
  where `alphas = np.logspace(-3, 3, 10)`, and
  where `rocket` depends on params `rocket_transform`, `use_multivariate` as follows:
  
      | rocket_transform | `use_multivariate` | rocket (class)          |
      |------------------|--------------------|-------------------------|
      | "rocket"         | any                | Rocket                  |
      | "minirocket"     | "yes               | MiniRocketMultivariate  |
      | "minirocket"     | "no"               | MiniRocket              |
      | "multirocket"    | "yes"              | MultiRocketMultivariate |
      | "multirocket"    | "no"               | MultiRocket             |
  
  classes are sktime classes, other parameters are passed on to the rocket class.
  
  To build other regressors with rocket transformers, use `make_pipeline` or the
  pipeline dunder `*`, and different transformers/regressors in combination.
  
  Parameters
  ----------
  num_kernels : int, optional, default=10,000
      The number of kernels the for Rocket transform.
  rocket_transform : str, optional, default="rocket"
      The type of Rocket transformer to use.
      Valid inputs = ["rocket", "minirocket", "multirocket"]
  max_dilations_per_kernel : int, optional, default=32
      MiniRocket and MultiRocket only. The maximum number of dilations per kernel.
  n_features_per_kernel : int, optional, default=4
      MultiRocket only. The number of features per kernel.
  use_multivariate : str, ["auto", "yes", "no"], optional, default="auto"
      whether to use multivariate rocket transforms or univariate ones
      "auto" = multivariate iff data seen in fit is multivariate, otherwise univariate
      "yes" = always uses multivariate transformers, native multi/univariate
      "no" = always univariate transformers, multivariate by framework vectorization
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  random_state : int or None, default=None
      Seed for random number generation.
  
  Attributes
  ----------
  n_classes : int
      The number of classes.
  classes_ : list
      The classes labels.
  estimator_ : RegressorPipeline
      RocketRegressor as a RegressorPipeline, fitted to data internally
  
  See Also
  --------
  Rocket, RocketClassifier
  
  References
  ----------
  .. [1] Dempster, Angus, François Petitjean, and Geoffrey I. Webb. "Rocket:
     exceptionally fast and accurate time series classification using random
     convolutional kernels." Data Mining and Knowledge Discovery 34.5 (2020)
  
  Examples
  --------
  >>> from sktime.regression.kernel_based import RocketRegressor
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
  >>> X_test, y_test = load_unit_test(split="test", return_X_y=True) # doctest: +SKIP
  >>> reg = RocketRegressor(num_kernels=500) # doctest: +SKIP
  >>> reg.fit(X_train, y_train) # doctest: +SKIP
  RocketRegressor(...)
  >>> y_pred = reg.predict(X_test) # doctest: +SKIP
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docstr: Shorthand for the internal estimator that is fitted.
docstr_tok: ["Shorthand","for","the","internal","estimator","that","is","fitted","."]
code: def estimator_(self):
      """Shorthand for the internal estimator that is fitted."""
      return self._get_delegate()
  
file_path: sktime/sktime/regression/kernel_based/_rocket_regressor.py
imports: import numpy
  from sklearn.linear_model import RidgeCV
  from sklearn.preprocessing import StandardScaler
  from sktime.pipeline import make_pipeline
  from sktime.regression._delegate import _DelegatedRegressor
  from sktime.regression.base import BaseRegressor
  from sktime.transformations.panel.rocket import MiniRocket, MiniRocketMultivariate, MultiRocket, MultiRocketMultivariate, Rocket
label_desc: Use image node_id 2 for calling the RocketRegressor obj's underlying member method code with example usage: obj.estimator_() and returns: self