Levels of Confidence and Utility for Binary Classifiers

Zhiyi Zhang ()
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Zhiyi Zhang: The University of North Carolina at Charlotte, Charlotte, NC 28223, USA

Stats, 2024, vol. 7, issue 4, 1-17

Abstract: Two performance measures for binary tree classifiers are introduced: the level of confidence and the level of utility. Both measures are probabilities of desirable events in the construction process of a classifier and hence are easily and intuitively interpretable. The statistical estimation of these measures is discussed. The usual maximum likelihood estimators are shown to have upward biases, and an entropy-based bias-reducing methodology is proposed. Along the way, the basic question of appropriate sample sizes at tree nodes is considered.

Keywords: binary classifiers; tree classifiers; level of confidence; level of utility; entropies; the binomial distributions; the entropic binomial distributions; the maximum likelihood estimators; the entropic maximum likelihood estimators (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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