class_docstr: Anomaly filter base class.
  
  An anomaly filter has the ability to classify an anomaly score as anomalous or not. It can then
  be used to filter anomalies, in particular as part of a pipeline.
  
  Parameters
  ----------
  anomaly_detector
      An anomaly detector wrapped by the anomaly filter.
  protect_anomaly_detector
      Indicates whether or not the anomaly detector should be updated when the anomaly score is
      anomalous. If the data contains sporadic anomalies, then the anomaly detector should likely
      not be updated. Indeed, if it learns the anomaly score, then it will slowly start to
      consider anomalous anomaly scores as normal. This might be desirable, for instance in the
      case of drift.
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docstr: Classify an anomaly score as anomalous or not.
  
  Parameters
  ----------
  score
      An anomaly score to classify.
  
  Returns
  -------
  A boolean value indicating whether the anomaly score is anomalous or not.
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code: def classify(self, score: float) -> bool:
      """Classify an anomaly score as anomalous or not.
  
      Parameters
      ----------
      score
          An anomaly score to classify.
  
      Returns
      -------
      A boolean value indicating whether the anomaly score is anomalous or not.
  
      """
  
file_path: river/river/anomaly/base.py
imports: from __future__ import annotations
  import abc
  from river import base
label_desc: Use image node_id 3 for calling the AnomalyFilter obj's underlying member method code with example usage: obj.classify(score) without return types