class_docstr: Base class for estimators that use libsvm as backing library
  
  This implements support vector machine classification and regression.
  
  Parameter documentation is in the derived `SVC` class.
class_docstr_tok: ["Base","class","for","estimators","that","use","libsvm","as","backing","library","This","implements","support","vector","machine","classification","and","regression",".","Parameter","documentation","is","in","the","derived","`","SVC","`","class","."]
code: def coef_(self):
      if self.kernel != "linear":
          raise ValueError(
              "coef_ is only available when using a " "linear kernel"
          )
  
      coef = self._get_coef()
  
      # coef_ being a read-only property, it's better to mark the value as
      # immutable to avoid hiding potential bugs for the unsuspecting user.
      if sp.issparse(coef):
          # sparse matrix do not have global flags
          coef.data.flags.writeable = False
      else:
          # regular dense array
          coef.flags.writeable = False
      return coef
  
imports: from __future__ import print_function
  import numpy
  import scipy.sparse
  import warnings
  from abc import ABCMeta, abstractmethod
  from .None import libsvm, liblinear
  from .None import libsvm_sparse
  from ..base import BaseEstimator, ClassifierMixin
  from ..preprocessing import LabelEncoder
  from ..utils.multiclass import _ovr_decision_function
  from ..utils import check_array, check_consistent_length, check_random_state
  from ..utils import column_or_1d, check_X_y
  from ..utils import compute_class_weight, deprecated
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import check_classification_targets
  from ..externals import six
  from ..exceptions import ChangedBehaviorWarning
  from ..exceptions import ConvergenceWarning
  from ..exceptions import NotFittedError
label_desc: Use image node_id 17 for calling the BaseLibSVM obj's underlying member method code with example usage: obj.coef_() and returns: coef