class_docstr: Nu-Support Vector Classification.
class_docstr_tok: ["Nu-Support","Vector","Classification","."]
code: def __init__(
      self,
      nu=0.5,
      kernel="rbf",
      *,
      degree=3,
      gamma="scale",
      coef0=0.0,
      tol=1e-3,
      shrinking=True,
      cache_size=200.0,
      max_iter=-1,
      tau=1e-12,
      class_weight=None,
      decision_function_shape="ovr",
      break_ties=False,
      algorithm="thunder",
      **kwargs,
  ):
      super().__init__(
          C=1.0,
          nu=nu,
          epsilon=0.0,
          kernel=kernel,
          degree=degree,
          gamma=gamma,
          coef0=coef0,
          tol=tol,
          shrinking=shrinking,
          cache_size=cache_size,
          max_iter=max_iter,
          tau=tau,
          class_weight=class_weight,
          decision_function_shape=decision_function_shape,
          break_ties=break_ties,
          algorithm=algorithm,
      )
      self.svm_type = SVMtype.nu_svc
  
imports: from abc import ABCMeta, abstractmethod
  from enum import Enum
  from numbers import Number, Real
  import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator
  from daal4py.sklearn._utils import sklearn_check_version
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted
  from ..common._mixin import ClassifierMixin, RegressorMixin
  from ..common._policy import _get_policy
  from ..datatypes import from_table, to_table
  from ..utils import _check_array, _check_n_features, _check_X_y, _column_or_1d, _validate_targets
label_desc: Use image node_id 1 to create a new NuSVC object from inherited base classes: ClassifierMixin, BaseSVM with example: obj = NuSVC(nu, kernel)