docstr: Method to calibrate the classifier's predicted probabilities.
  
  Parameters
  ----------
  x
      Array containing conditioning variables for each instance.
  y
      Boolean array marking the domain each instance belongs to (`0` for reference, `1` for test).
docstr_tok: ["Method","to","calibrate","the","classifier","'s","predicted","probabilities",".","Parameters","--","--","--","--","--","x","Array","containing","conditioning","variables","for","each","instance",".","y","Boolean","array","marking","the","domain","each","instance","belongs","to","(","`","0","`","for","reference",",","`","1","`","for","test",")","."]
code: def calibrate(self, x: np.ndarray, y: np.ndarray):
      """
      Method to calibrate the classifier's predicted probabilities.
  
      Parameters
      ----------
      x
          Array containing conditioning variables for each instance.
      y
          Boolean array marking the domain each instance belongs to (`0` for reference, `1` for test).
      """
      clf = CalibratedClassifierCV(
          self.clf, method=self.cal_method, cv="prefit"
      )
      clf.fit(x, y)
      self.clf = clf
  
file_path: alibi-detect/alibi_detect/cd/_domain_clf.py
imports: from abc import ABC, abstractmethod
  from typing import Callable
  import numpy
  from sklearn.svm import SVC
  from sklearn.calibration import CalibratedClassifierCV
label_desc: Use image node_id 3 for calling the _SVCDomainClf obj's underlying member method code with example usage: obj.calibrate(x, y) without return types