class_docstr: Nu-Support Vector Classification.
  
  Similar to SVC but uses a parameter to control the number of support
  vectors.
  
  The implementation is based on libsvm.
  
  Read more in the :ref:`User Guide <svm_classification>`.
  
  Parameters
  ----------
  nu : float, optional (default=0.5)
      An upper bound on the fraction of training errors and a lower
      bound of the fraction of support vectors. Should be in the
      interval (0, 1].
  
  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 precompute the kernel matrix.
  
  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 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 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 NuSVC
  >>> clf = NuSVC()
  >>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
  NuSVC(cache_size=200, class_weight=None, coef0=0.0,
        decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
        max_iter=-1, nu=0.5, probability=False, random_state=None,
        shrinking=True, tol=0.001, verbose=False)
  >>> print(clf.predict([[-0.8, -1]]))
  [1]
  
  See also
  --------
  SVC
      Support Vector Machine for classification using libsvm.
  
  LinearSVC
      Scalable linear Support Vector Machine for classification using
      liblinear.
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code: def __init__(
      self,
      nu=0.5,
      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(NuSVC, self).__init__(
          impl="nu_svc",
          kernel=kernel,
          degree=degree,
          gamma=gamma,
          coef0=coef0,
          tol=tol,
          C=0.0,
          nu=nu,
          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 NuSVC object from inherited base classes: BaseSVC with example: obj = NuSVC(nu, kernel, degree, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state)