class_docstr: Multi-label classifier.
class_docstr_tok: ["Multi-label","classifier","."]
docstr: Update the model with a set of features `x` and the labels `y`.
  
  Parameters
  ----------
  x
      A dictionary of features.
  y
      A dictionary of labels.
docstr_tok: ["Update","the","model","with","a","set","of","features","`","x","`","and","the","labels","`","y","`",".","Parameters","--","--","--","--","--","x","A","dictionary","of","features",".","y","A","dictionary","of","labels","."]
code: def learn_one(self, x: dict, y: dict[FeatureName, bool]):
      """Update the model with a set of features `x` and the labels `y`.
  
      Parameters
      ----------
      x
          A dictionary of features.
      y
          A dictionary of labels.
  
      """
  
file_path: river/river/base/multi_output.py
imports: from __future__ import annotations
  import abc
  from .estimator import Estimator
  from .typing import FeatureName, RegTarget
label_desc: Use image node_id 1 for calling the MultiLabelClassifier obj's underlying member method code with example usage: obj.learn_one(x, y) without return types