class_docstr: Multi-label classifier.
class_docstr_tok: ["Multi-label","classifier","."]
docstr: Predict the labels of a set of features `x`.
  
  Parameters
  ----------
  x
      A dictionary of features.
  
  Returns
  -------
  The predicted labels.
docstr_tok: ["Predict","the","labels","of","a","set","of","features","`","x","`",".","Parameters","--","--","--","--","--","x","A","dictionary","of","features",".","Returns","--","--","--","-","The","predicted","labels","."]
code: def predict_one(self, x: dict, **kwargs) -> dict[FeatureName, bool]:
      """Predict the labels of a set of features `x`.
  
      Parameters
      ----------
      x
          A dictionary of features.
  
      Returns
      -------
      The predicted labels.
  
      """
  
      probas = self.predict_proba_one(x, **kwargs)
  
      preds = {}
      for label_id, label_probas in probas.items():
          if not label_probas:
              continue
          preds[label_id] = max(label_probas, key=label_probas.get)  # type: ignore
  
      return preds
  
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 3 for calling the MultiLabelClassifier obj's underlying member method code with example usage: obj.predict_one(x) and returns: preds