class_docstr: Unsupervised Outlier Detection.
  
  Estimate the support of a high-dimensional distribution.
  
  The implementation is based on libsvm.
  
  Read more in the :ref:`User Guide <svm_outlier_detection>`.
  
  Parameters
  ----------
  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.
  
  nu : float, optional
      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]. By default 0.5
      will be taken.
  
  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'.
  
  tol : float, optional
      Tolerance for stopping criterion.
  
  shrinking : boolean, optional
      Whether to use the shrinking heuristic.
  
  cache_size : float, optional
      Specify the size of the kernel cache (in MB).
  
  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.
  
  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 = [nSV, n_features]
      Support vectors.
  
  dual_coef_ : array, shape = [n_classes-1, n_SV]
      Coefficients of the support vectors in the decision function.
  
  coef_ : array, shape = [n_classes-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_classes-1]
      Constants in decision function.
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code: def __init__(
      self,
      kernel="rbf",
      degree=3,
      gamma="auto",
      coef0=0.0,
      tol=1e-3,
      nu=0.5,
      shrinking=True,
      cache_size=200,
      verbose=False,
      max_iter=-1,
      random_state=None,
  ):
      super(OneClassSVM, self).__init__(
          "one_class",
          kernel,
          degree,
          gamma,
          coef0,
          tol,
          0.0,
          nu,
          0.0,
          shrinking,
          False,
          cache_size,
          None,
          verbose,
          max_iter,
          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 OneClassSVM object from inherited base classes: BaseLibSVM with example: obj = OneClassSVM(kernel, degree, gamma, coef0, tol, nu, shrinking, cache_size, verbose, max_iter, random_state)