docstr: A domain classifier using the scikit-learn Support Vector Classifier
  (:py:class:`~sklearn.svm.SVC`). An SVC is fitted on all the
  data, with scores (that optimise hinge loss) mapped onto probabilities using logistic regression.
  
  Parameters
  ----------
  kernel
      Kernel used to pre-compute the kernel matrix from data matrices.
  cal_method
      The method to be used to calibrate the detector. This should be a method accepted by the scikit-learn
      :py:class:`~sklearn.calibration.CalibratedClassifierCV` class.
  clf_kwargs
      A dictionary of keyword arguments to be passed to the :py:class:`~sklearn.svm.SVC` classifier.
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code: def __init__(
      self,
      kernel: Callable,
      cal_method: str = "sigmoid",
      clf_kwargs: dict = None,
  ):
      """
      A domain classifier using the scikit-learn Support Vector Classifier
      (:py:class:`~sklearn.svm.SVC`). An SVC is fitted on all the
      data, with scores (that optimise hinge loss) mapped onto probabilities using logistic regression.
  
      Parameters
      ----------
      kernel
          Kernel used to pre-compute the kernel matrix from data matrices.
      cal_method
          The method to be used to calibrate the detector. This should be a method accepted by the scikit-learn
          :py:class:`~sklearn.calibration.CalibratedClassifierCV` class.
      clf_kwargs
          A dictionary of keyword arguments to be passed to the :py:class:`~sklearn.svm.SVC` classifier.
      """
      self.kernel = kernel
      self.cal_method = cal_method
      clf_kwargs = clf_kwargs or {}
      self.clf = SVC(kernel=self.kernel, **clf_kwargs)
  
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 1 to create a new _SVCDomainClf object from inherited base classes: _DomainClf with example: obj = _SVCDomainClf(kernel, cal_method, clf_kwargs)