code: def fbeta_score(self, beta=1.0):
          r
          if beta <= 0:
              raise AttributeError('Beta must be a positive real value.')
          precision = self.precision()
          recall = self.recall()
          return (
              (1 + beta ** 2)
              * precision
              * recall
              / ((beta ** 2 * precision) + recall)
          )
docs: r"""Return :math:`F_{\beta}` score.
  
          :math:`F_{\beta}` for a positive real value :math:`\beta` "measures
          the effectiveness of retrieval with respect to a user who
          attaches :math:`\beta` times as much importance to recall as
          precision" (van Rijsbergen 1979)
  
          :math:`F_{\beta}` score is defined as:
          :math:`(1 + \beta^2) \cdot \frac{precision \cdot recall}
          {((\beta^2 \cdot precision) + recall)}`
  
          Cf. https://en.wikipedia.org/wiki/F1_score
  
          Parameters
          ----------
          beta : float
              The :math:`\beta` parameter in the above formula
  
          Returns
          -------
          float
              The :math:`F_{\beta}` of the confusion table
  
          Raises
          ------
          AttributeError
              Beta must be a positive real value
  
          Examples
          --------
          >>> ct = ConfusionTable(120, 60, 20, 30)
          >>> ct.fbeta_score()
          0.8275862068965518
          >>> ct.fbeta_score(beta=0.1)
          0.8565371024734982