class_docstr: A classifier.
class_docstr_tok: ["A","classifier","."]
docstr: Predict the probability of each label for a 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","for","a","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
  ) -> dict[base.typing.ClfTarget, float]:
      """Predict the probability of each label for a dictionary of features `x`.
  
      Parameters
      ----------
      x
          A dictionary of features.
  
      Returns
      -------
      A dictionary that associates a probability which each label.
  
      """
  
      # Some classifiers don't have the ability to output probabilities, and instead only
      # predict labels directly. Therefore, we cannot impose predict_proba_one as an abstract
      # method that each classifier has to implement. Instead, we raise an exception to indicate
      # that a classifier does not support predict_proba_one.
      raise NotImplementedError
  
file_path: river/river/base/classifier.py
imports: from __future__ import annotations
  import abc
  import typing
  from river import base
  from .None import estimator
label_desc: Use image node_id 2 for calling the Classifier obj's underlying member method code with example usage: obj.predict_proba_one(x) without return types