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: Trains the model using the given train set.
  
  Parameters
  ----------
  train_set : Dataset
      The dataset used for training the model.
  weights : np.ndarray[np.float32] or None, default=None
      Sample weights or None.
docstr_tok: ["Trains","the","model","using","the","given","train","set",".","Parameters","--","--","--","--","--","train_set",":","Dataset","The","dataset","used","for","training","the","model",".","weights",":","np.ndarray","[","np.float32","]","or","None",",","default=None","Sample","weights","or","None","."]
code: def fit(self, train_set, weights=None):
      """Trains the model using the given train set.
  
      Parameters
      ----------
      train_set : Dataset
          The dataset used for training the model.
      weights : np.ndarray[np.float32] or None, default=None
          Sample weights or None.
      """
      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 1 for calling the Classifier obj's underlying member method code with example usage: obj.fit(train_set, weights) without return types