class_docstr: Abstract base class for classifiers that can be used with the active learning components.
      
class_docstr_tok: ["Abstract","base","class","for","classifiers","that","can","be","used","with","the","active","learning","components","."]
docstr: Predicts the label distribution for each sample in the given dataset.
  
  Parameters
  ----------
  data_set : Dataset
      A dataset for which the labels are to be predicted.
docstr_tok: ["Predicts","the","label","distribution","for","each","sample","in","the","given","dataset",".","Parameters","--","--","--","--","--","data_set",":","Dataset","A","dataset","for","which","the","labels","are","to","be","predicted","."]
code: def predict_proba(self, data_set):
      """Predicts the label distribution for each sample in the given dataset.
  
      Parameters
      ----------
      data_set : Dataset
          A dataset for which the labels are to be predicted.
      """
      pass
  
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 3 for calling the Classifier obj's underlying member method code with example usage: obj.predict_proba(data_set) without return types