class_docstr: Multi-label classifier.
class_docstr_tok: ["Multi-label","classifier","."]
docstr: Predict the probability of each label appearing given dictionary of features `x`.
  
  Parameters
  ----------
  x
      A dictionary of features.
  
  Returns
  -------
  A dictionary that associates a probability which each label.
docstr_tok: ["Predict","the","probability","of","each","label","appearing","given","dictionary","of","features","`","x","`",".","Parameters","--","--","--","--","--","x","A","dictionary","of","features",".","Returns","--","--","--","-","A","dictionary","that","associates","a","probability","which","each","label","."]
code: def predict_proba_one(
      self, x: dict, **kwargs
  ) -> dict[FeatureName, dict[bool, float]]:
      """Predict the probability of each label appearing given dictionary of features `x`.
  
      Parameters
      ----------
      x
          A dictionary of features.
  
      Returns
      -------
      A dictionary that associates a probability which each label.
  
      """
  
      # In case the multi-label classifier does not support probabilities
      raise NotImplementedError
  
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 2 for calling the MultiLabelClassifier obj's underlying member method code with example usage: obj.predict_proba_one(x) without return types