code: def min_confidence(
      classifier: OneVsRestClassifier,
      X_pool: modALinput,
      n_instances: int = 1,
      random_tie_break: bool = False,
  ) -> np.ndarray:
      """
      MinConfidence query strategy for multilabel classification.
  
      For more details on this query strategy, see
      Esuli and Sebastiani., Active Learning Strategies for Multi-Label Text Classification
      (http://dx.doi.org/10.1007/978-3-642-00958-7_12)
  
      Args:
          classifier: The multilabel classifier for which the labels are to be queried.
          X_pool: The pool of samples to query from.
          random_tie_break: If True, shuffles utility scores to randomize the order. This
              can be used to break the tie when the highest utility score is not unique.
  
      Returns:
          The index of the instance from X_pool chosen to be labelled.
          The minimal confidence metric of the chosen instance.
  
      """
  
      classwise_confidence = classifier.predict_proba(X_pool)
      classwise_min = np.min(classwise_confidence, axis=1)
  
      if not random_tie_break:
          return multi_argmin(classwise_min, n_instances)
  
      return shuffled_argmin(classwise_min, n_instances)
  
file_path: modAL/modAL/multilabel.py
imports: from typing import Optional
  import numpy
  from sklearn.multiclass import OneVsRestClassifier
  from modAL.models import ActiveLearner
  from modAL.utils.data import modALinput
  from modAL.utils.selection import multi_argmax, multi_argmin, shuffled_argmax, shuffled_argmin
label_desc: Use image node_id 5 for calling a global function with example usage: min_confidence(classifier, X_pool, n_instances, random_tie_break) and returns: shuffled_argmin, multi_argmin