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_plot_params(self, *, ax=None, name=None):
      check_matplotlib_support(f"{self.__class__.__name__}.plot")
      import matplotlib.pyplot as plt
  
      if ax is None:
          _, ax = plt.subplots()
  
      name = self.estimator_name if name is None else name
      return ax, ax.figure, 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 1 for calling the _BinaryClassifierCurveDisplayMixin obj's underlying member method code with example usage: obj._validate_plot_params() and returns: ax,  ax,  name