code: def recall(self):
          r
          if self._tp + self._fn == 0:
              return float('NaN')
          return self._tp / (self._tp + self._fn)
docs: r"""Return recall.
  
          Recall is defined as :math:`\frac{tp}{tp + fn}`
  
          AKA sensitivity
  
          AKA true positive rate (TPR)
  
          Cf. https://en.wikipedia.org/wiki/Precision_and_recall
  
          Cf. https://en.wikipedia.org/wiki/Sensitivity_(test)
  
          Cf. https://en.wikipedia.org/wiki/Information_retrieval#Recall
  
          Returns
          -------
          float
              The recall of the confusion table
  
          Example
          -------
          >>> ct = ConfusionTable(120, 60, 20, 30)
          >>> ct.recall()
          0.8