| Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
|---|---|---|
| Article Number | 06004 | |
| Number of page(s) | 8 | |
| Section | T6 - Machine learning & analysis | |
| DOI | https://doi.org/10.1051/epjconf/201921406004 | |
| Published online | 17 September 2019 | |
https://doi.org/10.1051/epjconf/201921406004
Binary classifier metrics for optimizing HEP event selection
CERN, Information Technology Department,
CH-1211 Geneva 23,
Switzerland
* e-mail: andrea.valassi@cern.ch
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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