class_docstr: One-vs-the-rest (OvR) multiclass strategy.
  
  This strategy consists in fitting one binary classifier per class. Because we are in a
  streaming context, the number of classes isn't known from the start. Hence, new classifiers are
  instantiated on the fly. Likewise, the predicted probabilities will only include the classes
  seen up to a given point in time.
  
  Note that this classifier supports mini-batches as well as single instances.
  
  The computational complexity for both learning and predicting grows linearly with the number of
  classes. If you have a very large number of classes, then you might want to consider using an
  `multiclass.OutputCodeClassifier` instead.
  
  Parameters
  ----------
  classifier
      A binary classifier, although a multi-class classifier will work too.
  
  Attributes
  ----------
  classifiers : dict
      A mapping between classes and classifiers.
  
  Examples
  --------
  
  >>> from river import datasets
  >>> from river import evaluate
  >>> from river import linear_model
  >>> from river import metrics
  >>> from river import multiclass
  >>> from river import preprocessing
  
  >>> dataset = datasets.ImageSegments()
  
  >>> scaler = preprocessing.StandardScaler()
  >>> ovr = multiclass.OneVsRestClassifier(linear_model.LogisticRegression())
  >>> model = scaler | ovr
  
  >>> metric = metrics.MacroF1()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  MacroF1: 77.46%
  
  This estimator also also supports mini-batching.
  
  >>> for X in pd.read_csv(dataset.path, chunksize=64):
  ...     y = X.pop('category')
  ...     y_pred = model.predict_many(X)
  ...     model.learn_many(X, y)
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code: def _multiclass(self):
      return True
  
file_path: river/river/multiclass/ovr.py
imports: from __future__ import annotations
  import pandas
  from river import base, linear_model
label_desc: Use image node_id 3 for calling the OneVsRestClassifier obj's underlying member method code with example usage: obj._multiclass() and returns: True