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 <outlier_detection>`.
  
  Parameters
  ----------
  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 precompute the kernel matrix.
  
  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'.
  
  tol : float, default=1e-3
      Tolerance for stopping criterion.
  
  nu : float, 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]. By default 0.5
      will be taken.
  
  shrinking : bool, default=True
      Whether to use the shrinking heuristic.
      See the :ref:`User Guide <shrinking_svm>`.
  
  cache_size : float, default=200
      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, default=-1
      Hard limit on iterations within solver, or -1 for no limit.
  
  Attributes
  ----------
  class_weight_ : ndarray of shape (n_classes,)
      Multipliers of parameter C for each class.
      Computed based on the ``class_weight`` parameter.
  
      .. deprecated:: 1.2
          `class_weight_` was deprecated in version 1.2 and will be removed in 1.4.
  
  coef_ : ndarray of shape (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_`.
  
  dual_coef_ : ndarray of shape (1, n_SV)
      Coefficients of the support vectors in the decision function.
  
  fit_status_ : int
      0 if correctly fitted, 1 otherwise (will raise warning)
  
  intercept_ : ndarray of shape (1,)
      Constant in the 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_ : int
      Number of iterations run by the optimization routine to fit the model.
  
      .. versionadded:: 1.1
  
  n_support_ : ndarray of shape (n_classes,), dtype=int32
      Number of support vectors for each class.
  
  offset_ : float
      Offset used to define the decision function from the raw scores.
      We have the relation: decision_function = score_samples - `offset_`.
      The offset is the opposite of `intercept_` and is provided for
      consistency with other outlier detection algorithms.
  
      .. versionadded:: 0.20
  
  shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
      Array dimensions of training vector ``X``.
  
  support_ : ndarray of shape (n_SV,)
      Indices of support vectors.
  
  support_vectors_ : ndarray of shape (n_SV, n_features)
      Support vectors.
  
  See Also
  --------
  sklearn.linear_model.SGDOneClassSVM : Solves linear One-Class SVM using
      Stochastic Gradient Descent.
  sklearn.neighbors.LocalOutlierFactor : Unsupervised Outlier Detection using
      Local Outlier Factor (LOF).
  sklearn.ensemble.IsolationForest : Isolation Forest Algorithm.
  
  Examples
  --------
  >>> from sklearn.svm import OneClassSVM
  >>> X = [[0], [0.44], [0.45], [0.46], [1]]
  >>> clf = OneClassSVM(gamma='auto').fit(X)
  >>> clf.predict(X)
  array([-1,  1,  1,  1, -1])
  >>> clf.score_samples(X)
  array([1.7798..., 2.0547..., 2.0556..., 2.0561..., 1.7332...])
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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 7 for calling the OneClassSVM obj's underlying member method code with example usage: obj._more_tags() and returns: dict