Performance Metric Elicitation from Pairwise Classifier Comparisons
This addresses the challenge of aligning classifier optimization with practitioner preferences in binary classification, presenting a novel approach but with incremental theoretical contributions.
The paper tackles the problem of determining which performance metric a practitioner should optimize for binary classification by formalizing Metric Elicitation, using pairwise feedback to elicit linear and linear-fractional metrics with provably query-efficient algorithms and robustness to noise.
Given a binary prediction problem, which performance metric should the classifier optimize? We address this question by formalizing the problem of Metric Elicitation. The goal of metric elicitation is to discover the performance metric of a practitioner, which reflects her innate rewards (costs) for correct (incorrect) classification. In particular, we focus on eliciting binary classification performance metrics from pairwise feedback, where a practitioner is queried to provide relative preference between two classifiers. By exploiting key geometric properties of the space of confusion matrices, we obtain provably query efficient algorithms for eliciting linear and linear-fractional performance metrics. We further show that our method is robust to feedback and finite sample noise.