class_docstr: C-Support Vector Classification.
  
  The implementation is based on libsvm. The fit time scales at least
  quadratically with the number of samples and may be impractical
  beyond tens of thousands of samples. For large datasets
  consider using :class:`~sklearn.svm.LinearSVC` or
  :class:`~sklearn.linear_model.SGDClassifier` instead, possibly after a
  :class:`~sklearn.kernel_approximation.Nystroem` transformer or
  other :ref:`kernel_approximation`.
  
  The multiclass support is handled according to a one-vs-one scheme.
  
  For details on the precise mathematical formulation of the provided
  kernel functions and how `gamma`, `coef0` and `degree` affect each
  other, see the corresponding section in the narrative documentation:
  :ref:`svm_kernels`.
  
  Read more in the :ref:`User Guide <svm_classification>`.
  
  Parameters
  ----------
  C : float, default=1.0
      Regularization parameter. The strength of the regularization is
      inversely proportional to C. Must be strictly positive. The penalty
      is a squared l2 penalty.
  
  kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,          default='rbf'
      Specifies the kernel type to be used in the algorithm. If
      none is given, 'rbf' will be used. If a callable is given it is used to
      pre-compute the kernel matrix from data matrices; that matrix should be
      an array of shape ``(n_samples, n_samples)``. For an intuitive
      visualization of different kernel types see
      :ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`.
  
  degree : int, default=3
      Degree of the polynomial kernel function ('poly').
      Must be non-negative. Ignored by all other kernels.
  
  gamma : {'scale', 'auto'} or float, default='scale'
      Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
  
      - if ``gamma='scale'`` (default) is passed then it uses
        1 / (n_features * X.var()) as value of gamma,
      - if 'auto', uses 1 / n_features
      - if float, must be non-negative.
  
      .. versionchanged:: 0.22
         The default value of ``gamma`` changed from 'auto' to 'scale'.
  
  coef0 : float, default=0.0
      Independent term in kernel function.
      It is only significant in 'poly' and 'sigmoid'.
  
  shrinking : bool, default=True
      Whether to use the shrinking heuristic.
      See the :ref:`User Guide <shrinking_svm>`.
  
  probability : bool, default=False
      Whether to enable probability estimates. This must be enabled prior
      to calling `fit`, will slow down that method as it internally uses
      5-fold cross-validation, and `predict_proba` may be inconsistent with
      `predict`. Read more in the :ref:`User Guide <scores_probabilities>`.
  
  tol : float, default=1e-3
      Tolerance for stopping criterion.
  
  cache_size : float, default=200
      Specify the size of the kernel cache (in MB).
  
  class_weight : dict or 'balanced', default=None
      Set the parameter C of class i to class_weight[i]*C for
      SVC. If not given, all classes are supposed to have
      weight one.
      The "balanced" mode uses the values of y to automatically adjust
      weights inversely proportional to class frequencies in the input data
      as ``n_samples / (n_classes * np.bincount(y))``.
  
  verbose : bool, default=False
      Enable verbose output. Note that this setting takes advantage of a
      per-process runtime setting in libsvm that, if enabled, may not work
      properly in a multithreaded context.
  
  max_iter : int, default=-1
      Hard limit on iterations within solver, or -1 for no limit.
  
  decision_function_shape : {'ovo', 'ovr'}, default='ovr'
      Whether to return a one-vs-rest ('ovr') decision function of shape
      (n_samples, n_classes) as all other classifiers, or the original
      one-vs-one ('ovo') decision function of libsvm which has shape
      (n_samples, n_classes * (n_classes - 1) / 2). However, note that
      internally, one-vs-one ('ovo') is always used as a multi-class strategy
      to train models; an ovr matrix is only constructed from the ovo matrix.
      The parameter is ignored for binary classification.
  
      .. versionchanged:: 0.19
          decision_function_shape is 'ovr' by default.
  
      .. versionadded:: 0.17
         *decision_function_shape='ovr'* is recommended.
  
      .. versionchanged:: 0.17
         Deprecated *decision_function_shape='ovo' and None*.
  
  break_ties : bool, default=False
      If true, ``decision_function_shape='ovr'``, and number of classes > 2,
      :term:`predict` will break ties according to the confidence values of
      :term:`decision_function`; otherwise the first class among the tied
      classes is returned. Please note that breaking ties comes at a
      relatively high computational cost compared to a simple predict.
  
      .. versionadded:: 0.22
  
  random_state : int, RandomState instance or None, default=None
      Controls the pseudo random number generation for shuffling the data for
      probability estimates. Ignored when `probability` is False.
      Pass an int for reproducible output across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  Attributes
  ----------
  class_weight_ : ndarray of shape (n_classes,)
      Multipliers of parameter C for each class.
      Computed based on the ``class_weight`` parameter.
  
  classes_ : ndarray of shape (n_classes,)
      The classes labels.
  
  coef_ : ndarray of shape (n_classes * (n_classes - 1) / 2, n_features)
      Weights assigned to the features (coefficients in the primal
      problem). This is only available in the case of a linear kernel.
  
      `coef_` is a readonly property derived from `dual_coef_` and
      `support_vectors_`.
  
  dual_coef_ : ndarray of shape (n_classes -1, n_SV)
      Dual coefficients of the support vector in the decision
      function (see :ref:`sgd_mathematical_formulation`), multiplied by
      their targets.
      For multiclass, coefficient for all 1-vs-1 classifiers.
      The layout of the coefficients in the multiclass case is somewhat
      non-trivial. See the :ref:`multi-class section of the User Guide
      <svm_multi_class>` for details.
  
  fit_status_ : int
      0 if correctly fitted, 1 otherwise (will raise warning)
  
  intercept_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)
      Constants in decision function.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  n_iter_ : ndarray of shape (n_classes * (n_classes - 1) // 2,)
      Number of iterations run by the optimization routine to fit the model.
      The shape of this attribute depends on the number of models optimized
      which in turn depends on the number of classes.
  
      .. versionadded:: 1.1
  
  support_ : ndarray of shape (n_SV)
      Indices of support vectors.
  
  support_vectors_ : ndarray of shape (n_SV, n_features)
      Support vectors. An empty array if kernel is precomputed.
  
  n_support_ : ndarray of shape (n_classes,), dtype=int32
      Number of support vectors for each class.
  
  probA_ : ndarray of shape (n_classes * (n_classes - 1) / 2)
  probB_ : ndarray of shape (n_classes * (n_classes - 1) / 2)
      If `probability=True`, it corresponds to the parameters learned in
      Platt scaling to produce probability estimates from decision values.
      If `probability=False`, it's an empty array. Platt scaling uses the
      logistic function
      ``1 / (1 + exp(decision_value * probA_ + probB_))``
      where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For
      more information on the multiclass case and training procedure see
      section 8 of [1]_.
  
  shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
      Array dimensions of training vector ``X``.
  
  See Also
  --------
  SVR : Support Vector Machine for Regression implemented using libsvm.
  
  LinearSVC : Scalable Linear Support Vector Machine for classification
      implemented using liblinear. Check the See Also section of
      LinearSVC for more comparison element.
  
  References
  ----------
  .. [1] `LIBSVM: A Library for Support Vector Machines
      <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
  
  .. [2] `Platt, John (1999). "Probabilistic Outputs for Support Vector
      Machines and Comparisons to Regularized Likelihood Methods"
      <https://citeseerx.ist.psu.edu/doc_view/pid/42e5ed832d4310ce4378c44d05570439df28a393>`_
  
  Examples
  --------
  >>> import numpy as np
  >>> from sklearn.pipeline import make_pipeline
  >>> from sklearn.preprocessing import StandardScaler
  >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
  >>> y = np.array([1, 1, 2, 2])
  >>> from sklearn.svm import SVC
  >>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
  >>> clf.fit(X, y)
  Pipeline(steps=[('standardscaler', StandardScaler()),
                  ('svc', SVC(gamma='auto'))])
  
  >>> print(clf.predict([[-0.8, -1]]))
  [1]
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code: def _more_tags(self):
      return {
          "_xfail_checks": {
              "check_sample_weights_invariance": (
                  "zero sample_weight is not equivalent to removing samples"
              ),
          }
      }
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from ..base import BaseEstimator, OutlierMixin, RegressorMixin, _fit_context
  from ..linear_model._base import LinearClassifierMixin, LinearModel, SparseCoefMixin
  from ..utils import deprecated
  from ..utils._param_validation import Hidden, Interval, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _num_samples
  from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
label_desc: Use image node_id 2 for calling the SVC obj's underlying member method code with example usage: obj._more_tags() and returns: dict