class_docstr: Binary confusion matrix class
docstr: F1 score is the harmonic mean of precision and sensitivity
  F1 = 2 TP / (2 TP + FP + FN)
  can be also F1 = 2 * (precision * recall) / (precision + recall)
docstr_tok: ["F1","score","is","the","harmonic","mean","of","precision","and","sensitivity","F1","=","2","TP","\/","(","2","TP","+","FP","+","FN",")","can","be","also","F1","=","2","*","(","precision","*","recall",")","\/","(","precision","+","recall",")"]
code: def F1_score(self):
      """
      F1 score is the harmonic mean of precision and sensitivity
      F1 = 2 TP / (2 TP + FP + FN)
      can be also F1 = 2 * (precision * recall) / (precision + recall)
      """
      return 2 * np.float64(self.TP) / (2 * self.TP + self.FP + self.FN)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import math
  import numpy
  import pandas
  import collections
  from pandas_ml.confusion_matrix.abstract import ConfusionMatrixAbstract
label_desc: Use image node_id 34 for calling the BinaryConfusionMatrix obj's underlying member method code with example usage: obj.F1_score() and returns: unknown