class_docstr: Compatibility layer from scikit-learn to River for regression.
  
  Parameters
  ----------
  estimator
      A scikit-learn transformer which has a `partial_fit` method.
  
  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_diabetes(),
  ...     shuffle=True,
  ...     seed=42
  ... )
  
  >>> scaler = preprocessing.StandardScaler()
  >>> sgd_reg = compat.convert_sklearn_to_river(linear_model.SGDRegressor())
  >>> model = scaler | sgd_reg
  
  >>> metric = metrics.MAE()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  MAE: 84.501421
code: def predict_one(self, x):
      try:
          return self.estimator.predict(X=[self._align_dict(x)])[0]
      except sklearn_exceptions.NotFittedError:
          return 0
  
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 3 for calling the SKL2RiverRegressor obj's underlying member method code with example usage: obj.predict_one(x) and returns: self, int