class_docstr: Wrapper class for scikit-learn regression models.
docstr: Create a `Regressor` instance from a scikit-learn regressor model.
  
  :param model: scikit-learn regressor model.
  :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
         for features.
  :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
  :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
  :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
         used for data preprocessing. The first value will be subtracted from the input. The input will then
         be divided by the second one.
docstr_tok: ["Create","a","`","Regressor","`","instance","from","a","scikit-learn","regressor","model",".",":","param","model",":","scikit-learn","regressor","model",".",":","param","clip_values",":","Tuple","of","the","form","`","(","min",",","max",")","`","representing","the","minimum","and","maximum","values","allowed","for","features",".",":","param","preprocessing_defences",":","Preprocessing","defence","(","s",")","to","be","applied","by","the","classifier",".",":","param","postprocessing_defences",":","Postprocessing","defence","(","s",")","to","be","applied","by","the","classifier",".",":","param","preprocessing",":","Tuple","of","the","form","`","(","subtrahend",",","divisor",")","`","of","floats","or","`","np.ndarray","`","of","values","to","be","used","for","data","preprocessing",".","The","first","value","will","be","subtracted","from","the","input",".","The","input","will","then","be","divided","by","the","second","one","."]
code: def __init__(
      self,
      model: "sklearn.base.BaseEstimator",
      clip_values: Optional["CLIP_VALUES_TYPE"] = None,
      preprocessing_defences: Union[
          "Preprocessor", List["Preprocessor"], None
      ] = None,
      postprocessing_defences: Union[
          "Postprocessor", List["Postprocessor"], None
      ] = None,
      preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
  ) -> None:
      """
      Create a `Regressor` instance from a scikit-learn regressor model.
  
      :param model: scikit-learn regressor model.
      :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
             for features.
      :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
      :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
      :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
             used for data preprocessing. The first value will be subtracted from the input. The input will then
             be divided by the second one.
      """
      super().__init__(
          model=model,
          clip_values=clip_values,
          preprocessing_defences=preprocessing_defences,
          postprocessing_defences=postprocessing_defences,
          preprocessing=preprocessing,
      )
      self._input_shape = self._get_input_shape(model)
  
imports: import logging
  import os
  import pickle
  from copy import deepcopy
  from typing import List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.estimators.scikitlearn import ScikitlearnEstimator
  from art.estimators.regression.regressor import RegressorMixin
  from art import config
label_desc: Use image node_id 1 to create a new ScikitlearnRegressor object from inherited base classes: RegressorMixin, ScikitlearnEstimator with example: obj = ScikitlearnRegressor(model, clip_values, preprocessing_defences, postprocessing_defences, preprocessing)