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 labels for each sample in the given dataset.
  
  Parameters
  ----------
  data_set : Dataset
      A dataset for which the labels are to be predicted.
  return_proba : bool, default=False
      If `True`, also returns a probability-like class distribution.
docstr_tok: ["Predicts","the","labels","for","each","sample","in","the","given","dataset",".","Parameters","--","--","--","--","--","data_set",":","Dataset","A","dataset","for","which","the","labels","are","to","be","predicted",".","return_proba",":","bool",",","default=False","If","`","True","`",",","also","returns","a","probability-like","class","distribution","."]
code: def predict(self, data_set, return_proba=False):
      """Predicts the labels for each sample in the given dataset.
  
      Parameters
      ----------
      data_set : Dataset
          A dataset for which the labels are to be predicted.
      return_proba : bool, default=False
          If `True`, also returns a probability-like class distribution.
      """
      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 2 for calling the Classifier obj's underlying member method code with example usage: obj.predict(data_set, return_proba) without return types