class_docstr: A data interface for public data. This class is an interface to DiCE explainers
  and contains methods to transform user-fed raw data into the format a DiCE explainer
  requires, and vice versa.
docstr: Computes Median Absolute Deviation of features.
code: def get_mads(self, normalized=False):
      """Computes Median Absolute Deviation of features."""
      mads = {}
      if normalized is False:
          for feature in self.continuous_feature_names:
              mads[feature] = np.median(
                  abs(
                      self.data_df[feature].values
                      - np.median(self.data_df[feature].values)
                  )
              )
      else:
          normalized_train_df = self.normalize_data(self.data_df)
          for feature in self.continuous_feature_names:
              mads[feature] = np.median(
                  abs(
                      normalized_train_df[feature].values
                      - np.median(normalized_train_df[feature].values)
                  )
              )
      return mads
  
imports: import logging
  from collections import defaultdict
  import numpy
  import pandas
  from raiutils.exceptions import UserConfigValidationException
  from sklearn.preprocessing import LabelEncoder
  from dice_ml.data_interfaces.base_data_interface import _BaseData
  from dice_ml.utils.exception import SystemException
label_desc: Use image node_id 13 for calling the PublicData obj's underlying member method code with example usage: obj.get_mads(normalized) and returns: mads