class_docstr: C-Support Vector Classification.
  
  The implementation is based on libsvm. The fit time complexity
  is more than quadratic with the number of samples which makes it hard
  to scale to dataset with more than a couple of 10000 samples.
  
  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, optional (default=1.0)
      Penalty parameter C of the error term.
  
  kernel : string, optional (default='rbf')
       Specifies the kernel type to be used in the algorithm.
       It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
       a callable.
       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)``.
  
  degree : int, optional (default=3)
      Degree of the polynomial kernel function ('poly').
      Ignored by all other kernels.
  
  gamma : float, optional (default='auto')
      Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
      If gamma is 'auto' then 1/n_features will be used instead.
  
  coef0 : float, optional (default=0.0)
      Independent term in kernel function.
      It is only significant in 'poly' and 'sigmoid'.
  
  probability : boolean, optional (default=False)
      Whether to enable probability estimates. This must be enabled prior
      to calling `fit`, and will slow down that method.
  
  shrinking : boolean, optional (default=True)
      Whether to use the shrinking heuristic.
  
  tol : float, optional (default=1e-3)
      Tolerance for stopping criterion.
  
  cache_size : float, optional
      Specify the size of the kernel cache (in MB).
  
  class_weight : {dict, 'balanced'}, optional
      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, optional (default=-1)
      Hard limit on iterations within solver, or -1 for no limit.
  
  decision_function_shape : 'ovo', 'ovr' or None, default=None
      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).
      The default of None will currently behave as 'ovo' for backward
      compatibility and raise a deprecation warning, but will change 'ovr'
      in 0.19.
  
      .. versionadded:: 0.17
         *decision_function_shape='ovr'* is recommended.
  
      .. versionchanged:: 0.17
         Deprecated *decision_function_shape='ovo' and None*.
  
  random_state : int seed, RandomState instance, or None (default)
      The seed of the pseudo random number generator to use when
      shuffling the data for probability estimation.
  
  Attributes
  ----------
  support_ : array-like, shape = [n_SV]
      Indices of support vectors.
  
  support_vectors_ : array-like, shape = [n_SV, n_features]
      Support vectors.
  
  n_support_ : array-like, dtype=int32, shape = [n_class]
      Number of support vectors for each class.
  
  dual_coef_ : array, shape = [n_class-1, n_SV]
      Coefficients of the support vector in the decision function.
      For multiclass, coefficient for all 1-vs-1 classifiers.
      The layout of the coefficients in the multiclass case is somewhat
      non-trivial. See the section about multi-class classification in the
      SVM section of the User Guide for details.
  
  coef_ : array, shape = [n_class-1, 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_`.
  
  intercept_ : array, shape = [n_class * (n_class-1) / 2]
      Constants in decision function.
  
  Examples
  --------
  >>> import numpy as np
  >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
  >>> y = np.array([1, 1, 2, 2])
  >>> from sklearn.svm import SVC
  >>> clf = SVC()
  >>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
  SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
      decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
      max_iter=-1, probability=False, random_state=None, shrinking=True,
      tol=0.001, verbose=False)
  >>> print(clf.predict([[-0.8, -1]]))
  [1]
  
  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.
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code: def __init__(
      self,
      C=1.0,
      kernel="rbf",
      degree=3,
      gamma="auto",
      coef0=0.0,
      shrinking=True,
      probability=False,
      tol=1e-3,
      cache_size=200,
      class_weight=None,
      verbose=False,
      max_iter=-1,
      decision_function_shape=None,
      random_state=None,
  ):
      super(SVC, self).__init__(
          impl="c_svc",
          kernel=kernel,
          degree=degree,
          gamma=gamma,
          coef0=coef0,
          tol=tol,
          C=C,
          nu=0.0,
          shrinking=shrinking,
          probability=probability,
          cache_size=cache_size,
          class_weight=class_weight,
          verbose=verbose,
          max_iter=max_iter,
          decision_function_shape=decision_function_shape,
          random_state=random_state,
      )
  
imports: import warnings
  import numpy
  from .base import _fit_liblinear, BaseSVC, BaseLibSVM
  from ..base import BaseEstimator, RegressorMixin
  from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, LinearModel
  from ..feature_selection.from_model import _LearntSelectorMixin
  from ..utils import check_X_y
  from ..utils.validation import _num_samples
  from ..utils.multiclass import check_classification_targets
label_desc: Use image node_id 1 to create a new SVC object from inherited base classes: BaseSVC with example: obj = SVC(C, kernel, degree, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state)