class_docstr: Base class for active learning classifiers.
  
  Parameters
  ----------
  classifier
      The classifier to wrap.
  seed
      Random number generator seed for reproducibility.
class_docstr_tok: ["Base","class","for","active","learning","classifiers",".","Parameters","--","--","--","--","--","classifier","The","classifier","to","wrap",".","seed","Random","number","generator","seed","for","reproducibility","."]
docstr: Predict the label of `x` and indicate whether a label is needed.
  
  Parameters
  ----------
  x
      A dictionary of features.
  
  Returns
  -------
  The predicted label.
  A boolean indicating whether a label is needed.
docstr_tok: ["Predict","the","label","of","`","x","`","and","indicate","whether","a","label","is","needed",".","Parameters","--","--","--","--","--","x","A","dictionary","of","features",".","Returns","--","--","--","-","The","predicted","label",".","A","boolean","indicating","whether","a","label","is","needed","."]
code: def predict_one(self, x, **kwargs):
      """Predict the label of `x` and indicate whether a label is needed.
  
      Parameters
      ----------
      x
          A dictionary of features.
  
      Returns
      -------
      The predicted label.
      A boolean indicating whether a label is needed.
  
      """
      y_pred, ask_for_label = self.predict_proba_one(x, **kwargs)
      if y_pred:
          y_pred = max(y_pred, key=y_pred.get)  # type: ignore
      return y_pred, ask_for_label
  
file_path: river/river/active/base.py
imports: from __future__ import annotations
  import abc
  import random
  from river import base
label_desc: Use image node_id 5 for calling the ActiveLearningClassifier obj's underlying member method code with example usage: obj.predict_one(x) and returns: y_pred,  ask_for_label