class_docstr: Extends scikit-learn's LinearSVC class to provide confidence estimates.
      
class_docstr_tok: ["Extends","scikit-learn","'s","LinearSVC","class","to","provide","confidence","estimates","."]
docstr: Parameters
  ----------
  linearsvc_kwargs : dict, default=None
      Kwargs for the LinearSVC superclass.
docstr_tok: ["Parameters","--","--","--","--","--","linearsvc_kwargs",":","dict",",","default=None","Kwargs","for","the","LinearSVC","superclass","."]
code: def __init__(self, linearsvc_kwargs=None):
      """
      Parameters
      ----------
      linearsvc_kwargs : dict, default=None
          Kwargs for the LinearSVC superclass.
      """
      self.linearsvc_kwargs = (
          dict() if linearsvc_kwargs is None else linearsvc_kwargs
      )
      super().__init__(**self.linearsvc_kwargs)
  
imports: from abc import ABC, abstractmethod
  import numpy
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.preprocessing import normalize
  from sklearn.svm import LinearSVC
  from sklearn.utils.multiclass import is_multilabel
  from small_text.utils.classification import empty_result, prediction_result
  from small_text.utils.data import check_training_data
label_desc: Use image node_id 1 to create a new ConfidenceEnhancedLinearSVC object from inherited base classes: LinearSVC with example: obj = ConfidenceEnhancedLinearSVC(linearsvc_kwargs)