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 raw_preds(self, X):
      check_is_fitted(self, ["tree_", "resultType", "feature_names"])
      X = check_array(X)
      if isinstance(X, pd.DataFrame):
          X = deepcopy(X)
          X.columns = self.feature_names
      root = self.root
      prediction = []
      for i in range(X.shape[0]):
          answerlist = decision(root, X[i], self.feature_names, 1)
          answerlist = sorted(
              answerlist.items(), key=lambda x: x[1], reverse=True
          )
          answer = answerlist[0][0]
          # prediction.append(self.resultType(answer))
          prediction.append(float(answer))
  
      return np.array(prediction)
  
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 5 for calling the C45TreeClassifier obj's underlying member method code with example usage: obj.raw_preds(X) and returns: np