class_docstr: A data interface for private data with meta information.
class_docstr_tok: ["A","data","interface","for","private","data","with","meta","information","."]
docstr: Computes Median Absolute Deviation of features.
docstr_tok: ["Computes","Median","Absolute","Deviation","of","features","."]
code: def get_mads(self, normalized=True):
      """Computes Median Absolute Deviation of features."""
      if normalized is False:
          return self.mad.copy()
      else:
          mads = {}
          for feature in self.continuous_feature_names:
              if feature in self.mad:
                  mads[feature] = (self.mad[feature]) / (
                      self.permitted_range[feature][1]
                      - self.permitted_range[feature][0]
                  )
          return mads
  
imports: import collections
  import logging
  import sys
  from collections import defaultdict
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from dice_ml.data_interfaces.base_data_interface import _BaseData
label_desc: Use image node_id 8 for calling the PrivateData obj's underlying member method code with example usage: obj.get_mads(normalized) and returns: self, mads