class_docstr: Bias subset scanning is proposed as a technique to identify bias in
  predictive models using subset scanning [#zhang16]_.
  
  This class is a wrapper for the bias scan scoring and scanning methods that
  uses the ClassificationMetric abstraction.
  
  References:
      .. [#zhang16] `Zhang, Z. and Neill, D. B., "Identifying significant
         predictive bias in classifiers," arXiv preprint, 2016.
         <https://arxiv.org/abs/1611.08292>`_
docstr: Args:
      dataset (BinaryLabelDataset): Dataset containing ground-truth
          labels.
      classified_dataset (BinaryLabelDataset): Dataset containing
          predictions.
      scoring (str or ScoringFunction): One of 'Bernoulli' (parametric), or 'BerkJones' (non-parametric)
                  or subclass of :class:`aif360.metrics.mdss.ScoringFunctions.ScoringFunction`.
                  Defaults to Bernoulli.
      privileged_groups (list(dict)): Privileged groups. Format is a list
          of `dicts` where the keys are `protected_attribute_names` and
          the values are values in `protected_attributes`. Each `dict`
          element describes a single group. See examples for more details.
      unprivileged_groups (list(dict)): Unprivileged groups in the same
          format as `privileged_groups`.
code: def __init__(
      self,
      dataset: BinaryLabelDataset,
      classified_dataset: BinaryLabelDataset,
      scoring: Union[str, ScoringFunction] = "Bernoulli",
      unprivileged_groups: dict = None,
      privileged_groups: dict = None,
      **kwargs,
  ):
      """
      Args:
          dataset (BinaryLabelDataset): Dataset containing ground-truth
              labels.
          classified_dataset (BinaryLabelDataset): Dataset containing
              predictions.
          scoring (str or ScoringFunction): One of 'Bernoulli' (parametric), or 'BerkJones' (non-parametric)
                      or subclass of :class:`aif360.metrics.mdss.ScoringFunctions.ScoringFunction`.
                      Defaults to Bernoulli.
          privileged_groups (list(dict)): Privileged groups. Format is a list
              of `dicts` where the keys are `protected_attribute_names` and
              the values are values in `protected_attributes`. Each `dict`
              element describes a single group. See examples for more details.
          unprivileged_groups (list(dict)): Unprivileged groups in the same
              format as `privileged_groups`.
      """
  
      super(MDSSClassificationMetric, self).__init__(
          dataset,
          classified_dataset,
          unprivileged_groups=unprivileged_groups,
          privileged_groups=privileged_groups,
      )
  
      self.scoring = scoring
      self.kwargs = kwargs
  
file_path: AIF360/aif360/metrics/mdss_classification_metric.py
imports: from typing import Union
  from aif360.datasets import BinaryLabelDataset
  from aif360.metrics import ClassificationMetric
  from aif360.detectors.mdss.ScoringFunctions import Bernoulli, BerkJones, ScoringFunction
  from aif360.detectors.mdss.MDSS import MDSS
  import pandas
label_desc: Use image node_id 1 to create a new MDSSClassificationMetric object from inherited base classes: ClassificationMetric with example: obj = MDSSClassificationMetric(dataset, classified_dataset, scoring, unprivileged_groups, privileged_groups)