Binary classifier metrics for optimizing HEP event selection
CERN, Information Technology Department,
CH-1211 Geneva 23,
* e-mail: firstname.lastname@example.org
Published online: 17 September 2019
I discuss the choice of evaluation metrics for binary classifiers in High Energy Physics (HEP) event selection and I point out that the Area Under the ROC Curve (AUC) is of limited relevance in this context, after discussing its use in other domains. I propose new metrics based on Fisher information, which can be used for both the evaluation and training of HEP event selection algorithms in statistically limited measurements of a parameter.
© The Authors, published by EDP Sciences, 2019
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