class_docstr: Mixin class to be used in Displays requiring a binary classifier.
  
  The aim of this class is to centralize some validations regarding the estimator and
  the target and gather the response of the estimator.
class_docstr_tok: ["Mixin","class","to","be","used","in","Displays","requiring","a","binary","classifier",".","The","aim","of","this","class","is","to","centralize","some","validations","regarding","the","estimator","and","the","target","and","gather","the","response","of","the","estimator","."]
code: def _validate_and_get_response_values(
      cls,
      estimator,
      X,
      y,
      *,
      response_method="auto",
      pos_label=None,
      name=None,
  ):
      check_matplotlib_support(f"{cls.__name__}.from_estimator")
  
      name = estimator.__class__.__name__ if name is None else name
  
      y_pred, pos_label = _get_response_values_binary(
          estimator,
          X,
          response_method=response_method,
          pos_label=pos_label,
      )
  
      return y_pred, pos_label, name
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/utils/_plotting.py
imports: import numpy
  from .None import check_consistent_length, check_matplotlib_support
  from ._response import _get_response_values_binary
  from .multiclass import type_of_target
  from .validation import _check_pos_label_consistency
label_desc: Use image node_id 2 for calling the _BinaryClassifierCurveDisplayMixin obj's underlying member method code with example usage: obj._validate_and_get_response_values(cls, estimator, X, y) and returns: y_pred,  pos_label,  name