docstr: Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args.
  Cross-validation is used to select the best regularization parameter for hierarchical shrinkage.
docstr_tok: ["Note",":","args",",","kwargs","are","not","used","but","left","so","that","imodels-experiments","can","still","pass","redundant","args",".","Cross-validation","is","used","to","select","the","best","regularization","parameter","for","hierarchical","shrinkage","."]
code: def __init__(
      self,
      estimator_: BaseEstimator,
      reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500],
      shrinkage_scheme_: str = "node_based",
      cv: int = 3,
      scoring=None,
      *args,
      **kwargs,
  ):
      """Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args.
      Cross-validation is used to select the best regularization parameter for hierarchical shrinkage.
      """
      super().__init__(estimator_, reg_param=None)
      self.reg_param_list = np.array(reg_param_list)
      self.cv = cv
      self.scoring = scoring
      self.shrinkage_scheme_ = shrinkage_scheme_
  
imports: from copy import deepcopy
  from typing import List
  import numpy
  from sklearn import datasets
  from sklearn.base import BaseEstimator
  from sklearn.metrics import r2_score
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeRegressor
  from imodels.util import checks
label_desc: Use image node_id 1 to create a new HSFIGSClassifierCV object from inherited base classes: HSFIGSClassifier with example: obj = HSFIGSClassifierCV(estimator_, reg_param_list, shrinkage_scheme_, cv, scoring)