class_docstr: Compatibility layer from scikit-learn to River for classification.
  
  Parameters
  ----------
  estimator
      A scikit-learn regressor which has a `partial_fit` method.
  classes
  
  Examples
  --------
  
  >>> from river import compat
  >>> from river import evaluate
  >>> from river import metrics
  >>> from river import preprocessing
  >>> from river import stream
  >>> from sklearn import linear_model
  >>> from sklearn import datasets
  
  >>> dataset = stream.iter_sklearn_dataset(
  ...     dataset=datasets.load_breast_cancer(),
  ...     shuffle=True,
  ...     seed=42
  ... )
  
  >>> model = preprocessing.StandardScaler()
  >>> model |= compat.convert_sklearn_to_river(
  ...     estimator=linear_model.SGDClassifier(
  ...         loss='log_loss',
  ...         eta0=0.01,
  ...         learning_rate='constant'
  ...     ),
  ...     classes=[False, True]
  ... )
  
  >>> metric = metrics.LogLoss()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  LogLoss: 0.198029
code: def predict_proba_one(self, x):
      try:
          y_pred = self.estimator.predict_proba([self._align_dict(x)])[
              0
          ]
          return {self.classes[i]: p for i, p in enumerate(y_pred)}
      except sklearn_exceptions.NotFittedError:
          return {c: 1 / len(self.classes) for c in self.classes}
  
imports: from __future__ import annotations
  import copy
  import functools
  import pandas
  from sklearn import base
  from sklearn import exceptions
  from river import base
label_desc: Use image node_id 5 for calling the SKL2RiverClassifier obj's underlying member method code with example usage: obj.predict_proba_one(x) and returns: unknown, unknown