Optimal Thresholding of Classifiers to Maximize F1 Measure

@article{Lipton2014OptimalTO,
  title={Optimal Thresholding of Classifiers to Maximize F1 Measure},
  author={Zachary Chase Lipton and Charles Elkan and Balakrishnan Narayanaswamy},
  journal={Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD},
  year={2014},
  volume={8725},
  pages={225-239}
}
This paper provides new insight into maximizing F1 measures in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, the F1 measure is widely used to evaluate the success of a binary classifier when one class is rare. Micro average, macro average, and per instance average F1 measures are used in multilabel classification. For any classifier that produces a real-valued output, we derive the relationship between the… CONTINUE READING
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