docstr: Check if classifier passes various fairness criteria
  
  Fairness check is an easy way to check if the model is fair.
  For that, this method uses 3 non-discrimination criteria.
  The approximations are made to check the conditional independence expressed
  in form of independence, separation and sufficiency.
  Model is considered to be fair if all criteria are met.
  This arbitrary decision is based on epsilon,
  which defaults to `0.8` (it matches the four-fifths 80% rule).
  
  Methods in use: Independence, Separation, Sufficiency.
  
  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 criteria
  
      Fairness check is an easy way to check if the model is fair.
      For that, this method uses 3 non-discrimination criteria.
      The approximations are made to check the conditional independence expressed
      in form of independence, separation and sufficiency.
      Model is considered to be fair if all criteria are met.
      This arbitrary decision is based on epsilon,
      which defaults to `0.8` (it matches the four-fifths 80% rule).
  
      Methods in use: Independence, Separation, Sufficiency.
  
      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=1,
          num_for_no_decision=None,
          metrics=["independence", "separation", "sufficiency"],
      )
  
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 GroupFairnessRegression obj's underlying member method code with example usage: obj.fairness_check(epsilon, verbose) without return types