class_docstr: Binary F-Beta score.
  
  The FBeta score is a weighted harmonic mean between precision and recall. The higher the
  `beta` value, the higher the recall will be taken into account. When `beta` equals 1,
  precision and recall and equivalently weighted, which results in the F1 score (see
  `metrics.F1`).
  
  Parameters
  ----------
  beta
      Weight of precision in the harmonic mean.
  cm
      This parameter allows sharing the same confusion
      matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage
      and computation time.
  pos_val
      Value to treat as "positive".
  
  Attributes
  ----------
  precision : metrics.Precision
  recall : metrics.Recall
  
  Examples
  --------
  
  >>> from river import metrics
  
  >>> y_true = [False, False, False, True, True, True]
  >>> y_pred = [False, False, True, True, False, False]
  
  >>> metric = metrics.FBeta(beta=2)
  >>> for yt, yp in zip(y_true, y_pred):
  ...     metric.update(yt, yp)
  
  >>> metric
  FBeta: 35.71%
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code: def __init__(self, beta: float, cm=None, pos_val=True):
      super().__init__(cm, pos_val)
      self.beta = beta
      self.precision = metrics.Precision(self.cm, self.pos_val)
      self.recall = metrics.Recall(self.cm, self.pos_val)
  
imports: from __future__ import annotations
  import collections
  import functools
  from river import metrics
label_desc: Use image node_id 1 to create a new FBeta object from inherited base classes: metrics.base with example: obj = FBeta(beta, cm, pos_val)