class_docstr: A C4.5 tree classifier.
  
  Parameters
  ----------
  max_rules : int, optional (default=None)
      Maximum number of split nodes allowed in the tree
class_docstr_tok: ["A","C4.5","tree","classifier",".","Parameters","--","--","--","--","--","max_rules",":","int",",","optional","(","default=None",")","Maximum","number","of","split","nodes","allowed","in","the","tree"]
code: def fit(self, X, y, feature_names: str = None):
      self.complexity_ = 0
      # X, y = check_X_y(X, y)
      X, y, feature_names = check_fit_arguments(
          self, X, y, feature_names
      )
      self.resultType = type(y[0])
      if feature_names is None:
          self.feature_names = [f"X_{x}" for x in range(X.shape[1])]
      else:
          # only include alphanumeric chars / replace spaces with underscores
          self.feature_names = [
              "".join([i for i in x if i.isalnum()]).replace(" ", "_")
              for x in feature_names
          ]
          self.feature_names = [
              "X_" + x if x[0].isdigit() else x
              for x in self.feature_names
          ]
  
      assert len(self.feature_names) == X.shape[1]
  
      data = [[] for i in range(len(self.feature_names))]
      categories = []
  
      for i in range(len(X)):
          categories.append(str(y[i]))
          for j in range(len(self.feature_names)):
              data[j].append(X[i][j])
      root = ET.Element("GreedyTree")
      self.grow_tree(
          data, categories, root, self.feature_names
      )  # adds to root
      self.tree_ = ET.tostring(root, encoding="unicode")
      # print('self.tree_', self.tree_)
      self.dom_ = minidom.parseString(self.tree_)
      return self
  
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 2 for calling the C45TreeClassifier obj's underlying member method code with example usage: obj.fit(X, y, feature_names) and returns: self