Year: 2024
Type: article
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.
Source: Stats
DOI: 10.3390/stats7040071
Author: Zhiyi Zhang
Institution: University of North Carolina at Charlotte
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,819
OpenAlex ID: W4403483673