Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability
Table 1: Accuracy obtained by training an standalone classifier, applying the API and the proposed wrapper for each domain
['[EMPTY]', '[BOLD] BB source acc.', '[BOLD] BB target acc.', '[BOLD] Non-reject. acc. (10/20/30%)', '[BOLD] Class. quality (10/20/30%)', '[BOLD] Reject. quality (10/20/30%)']
Table 1 shows the numerical results obtained during the experiments for the four combinations tested. [CONTINUE] In general terms, the results displayed in table 1 show that the rejection method can reduce the error of the output predictions when applying a pre-trained black-box classification system to a new domain. [CONTINUE] Table 1: Accuracy obtained by training an standalone classifier, applying the API and the proposed wrapper for each domain