docstr: Check if classifier passes various fairness metrics
  
  Fairness check is an easy way to check if the model is fair.
  For that, this method uses 5 popular metrics of group fairness.
  Model is considered to be fair if confusion matrix metrics are
  close to each other. This arbitrary decision is based on epsilon,
  which defaults to `0.8` (it matches the four-fifths 80% rule).
  
  Methods in use: Equal opportunity, Predictive parity, Predictive equality,
  Statistical parity and Accuracy equality.
  
  Parameters
  -----------
  epsilon : float, optional
      Parameter defines acceptable fairness scores. The closer to `1` the
      more strict the verdict is. If the ratio of certain unprivileged
      and privileged subgroup is within the `(epsilon, 1/epsilon)` range,
      then there is no discrimination in this metric and for this subgroups
      (default is `0.8`, which is set during object initialization).
  verbose : bool
      Shows verbose text about potential problems 
      (e.g. `NaN` in model metrics that can cause misinterpretation).
  
  Returns
  -----------
  None (prints console output)
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code: def fairness_check(self, epsilon=None, verbose=True):
      """Check if classifier passes various fairness metrics
  
      Fairness check is an easy way to check if the model is fair.
      For that, this method uses 5 popular metrics of group fairness.
      Model is considered to be fair if confusion matrix metrics are
      close to each other. This arbitrary decision is based on epsilon,
      which defaults to `0.8` (it matches the four-fifths 80% rule).
  
      Methods in use: Equal opportunity, Predictive parity, Predictive equality,
      Statistical parity and Accuracy equality.
  
      Parameters
      -----------
      epsilon : float, optional
          Parameter defines acceptable fairness scores. The closer to `1` the
          more strict the verdict is. If the ratio of certain unprivileged
          and privileged subgroup is within the `(epsilon, 1/epsilon)` range,
          then there is no discrimination in this metric and for this subgroups
          (default is `0.8`, which is set during object initialization).
      verbose : bool
          Shows verbose text about potential problems
          (e.g. `NaN` in model metrics that can cause misinterpretation).
  
      Returns
      -----------
      None (prints console output)
  
      """
  
      utils.universal_fairness_check(
          self,
          epsilon,
          verbose,
          num_for_not_fair=2,
          num_for_no_decision=1,
          metrics=utils.fairness_check_metrics(),
      )
  
file_path: DALEX/python/dalex/dalex/fairness/_group_fairness/object.py
imports: from .None import checks, plot, utils
  from .._basics import checks
  from .._basics._base_objects import _FairnessObject
  from .._basics.exceptions import ParameterCheckError
  from ...None import _global_checks, _theme
label_desc: Use image node_id 2 for calling the GroupFairnessClassification obj's underlying member method code with example usage: obj.fairness_check(epsilon, verbose) without return types