docstr: Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args
          
docstr_tok: ["Note",":","args",",","kwargs","are","not","used","but","left","so","that","imodels-experiments","can","still","pass","redundant","args"]
code: def __init__(
      self,
      estimator_: C45TreeClassifier,
      reg_param_list: List[float] = [0.1, 1, 10, 50, 100, 500],
      shrinkage_scheme_: str = "node_based",
      cv: int = 3,
      scoring="accuracy",
      *args,
      **kwargs,
  ):
      """Note: args, kwargs are not used but left so that imodels-experiments can still pass redundant args"""
      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: import copy
  from copy import deepcopy
  from typing import List
  from xml.dom import minidom
  from xml.etree import ElementTree
  import numpy
  import pandas
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.model_selection import cross_val_score
  from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
  from imodels.util.arguments import check_fit_arguments
  from ..c45_tree.c45_utils import decision, is_numeric_feature, gain, gain_ratio, get_best_split, set_as_leaf_node
  from ...util.data_util import get_clean_dataset
label_desc: Use image node_id 1 to create a new HSC45TreeClassifierCV object from inherited base classes: HSC45TreeClassifier with example: obj = HSC45TreeClassifierCV(estimator_, reg_param_list, shrinkage_scheme_, cv, scoring)