code: def fbt_vs_fb_meps(
      iterations=30, treeNum=1, treeDepth=4, numThresh=9, filename=None
  ):
      X: pd.DataFrame = MEPSDataset().data()
      X.reset_index(drop=True, inplace=True)
      # Drop panel number (not meant to be predictive) and sample weights
      X.drop(columns=["PANEL", "PERSONWT"], inplace=True)
      y = X.pop("HEALTHEXP")
      categorical = [
          "REGION",
          "MARRY31X",
          "EDRECODE",
          "FTSTU31X",
          "ACTDTY31",
          "HONRDC31",
          "RTHLTH31",
          "MNHLTH31",
          "HIBPDX",
          "CHDDX",
          "ANGIDX",
          "MIDX",
          "OHRTDX",
          "STRKDX",
          "EMPHDX",
          "CHBRON31",
          "CHOLDX",
          "CANCERDX",
          "DIABDX",
          "JTPAIN31",
          "ARTHDX",
          "ARTHTYPE",
          "ASTHDX",
          "ADHDADDX",
          "PREGNT31",
          "WLKLIM31",
          "ACTLIM31",
          "SOCLIM31",
          "COGLIM31",
          "DFHEAR42",
          "DFSEE42",
          "ADSMOK42",
          "PHQ242",
          "EMPST31",
          "POVCAT15",
          "INSCOV15",
      ]
      X.drop(columns=get_corr_columns(X), inplace=True)
      y = (y > y.mean()).astype(int)
      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 5 for calling a global function with example usage: fbt_vs_fb_meps(iterations, treeNum, treeDepth, numThresh, filename) and returns: fbt_vs_fb