class_docstr: General wrapper class for sklearn models.
class_docstr_tok: ["General","wrapper","class","for","sklearn","models","."]
docstr: Clones the prediction model using the same hyper parameters but not fitted.
  :return: An unfitted clone of the prediction model.
docstr_tok: ["Clones","the","prediction","model","using","the","same","hyper","parameters","but","not","fitted",".",":","return",":","An","unfitted","clone","of","the","prediction","model","."]
code: def clone(self):
      """
      Clones the prediction model using the same hyper parameters but not fitted.
      :return: An unfitted clone of the prediction model.
      """
      return SklearnRegressionModel(
          sklearn_mdl=sklearn.clone(self._sklearn_mdl)
      )
  
imports: from abc import abstractmethod
  from typing import Any
  import numpy
  import sklearn
  from packaging import version
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn.ensemble import AdaBoostRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor, RandomForestRegressor
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.linear_model import ElasticNetCV, LassoCV, LassoLarsIC, LinearRegression, RidgeCV
  from sklearn.neighbors import KNeighborsRegressor
  from sklearn.svm import SVR
  from dowhy.gcm.ml.prediction_model import PredictionModel
  from dowhy.gcm.util.general import auto_apply_encoders, auto_fit_encoders, shape_into_2d
label_desc: Use image node_id 5 for calling the SklearnRegressionModel obj's underlying member method code with example usage: obj.clone() and returns: SklearnRegressionModel