code: def fbt_vs_fb_fico(
      iterations=30, treeNum=1, treeDepth=4, numThresh=9, filename=None
  ):
      # Load FICO HELOC data with special values converted to np.nan
      data: DataFrame = HELOCDataset(
          custom_preprocessing=nan_preprocessing
      ).data()
  
      # FeatureBinarizerFromTrees requires the user to decide how to handle NaN/None.
      s = data.isnull().sum()
      for col in s[s > 0].index:
          data[f"{col}-NaN"] = data[col].isnull().to_numpy()
      data.fillna(-9999, inplace=True)
  
      # Drop highly correlated columns
      data.drop(columns=get_corr_columns(data), inplace=True)
      y = data.pop("RiskPerformance")
      return fbt_vs_fb(
          X,
          y,
          iterations=iterations,
          treeNum=treeNum,
          treeDepth=treeDepth,
          numThresh=numThresh,
          filename=filename,
      )
  
imports: import warnings
  import pickle
  from time import time
  import numpy
  import pandas
  import shap
  from aix360.algorithms.rbm import BooleanRuleCG, BRCGExplainer, FeatureBinarizer, FeatureBinarizerFromTrees, GLRMExplainer, LogisticRuleRegression
  from aix360.datasets import HELOCDataset, MEPSDataset
  from aix360.datasets.heloc_dataset import nan_preprocessing
  from pandas import DataFrame
  from sklearn.datasets import load_breast_cancer
  from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 4 for calling a global function with example usage: fbt_vs_fb_fico(iterations, treeNum, treeDepth, numThresh, filename) and returns: fbt_vs_fb