The Fermi-Dirac distribution provides a calibrated probabilistic output for binary classifiers.

ABSTRACT: Binary classification is one of the central problems in machine-learning research and, as such, investigations of its general statistical properties are of interest. We studied the ranking statistics of items in binary classification problems and observed that there is a formal and surprising relationship between the probability of a sample belonging to one of the two classes and the Fermi-Dirac distribution determining the probability that a fermion occupies a given single-particle quantum state in a physical system of noninteracting fermions. Using this equivalence, it is possible to compute a calibrated probabilistic output for binary classifiers. We show that the area under the receiver operating characteristics curve (AUC) in a classification problem is related to the temperature of a

SUBMITTER: Kim SC 

PROVIDER: S-EPMC8403970 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature