Ningsheng Zhao | Searcharxiv

arXiv2026

Introducing the O-Value: A Universal Standardization for Confusion-Matrix-Based Classification Performance Metrics

Many classification performance metrics exist, each suited to a specific application. However, these metrics often differ in scale and can exhibit varying sensitivity to class imbalance rates in the test set. As a result, it is difficult to use the nominal values of these metrics to evaluate, compare and monitor classification performances, especially when imbalance rates vary. To address this problem, we introduce the outperformance standardization (OPS) function, a universal standardization method for confusion-matrix-based classification performance (CMBCP) metrics. It maps any given metric to a common scale of $[0,1]$, while providing a clear and consistent interpretation. Specifically, the resulting OPS value (o-value) represents the percentile rank of the observed classification performance within a reference distribution of possible performances. This unified framework enables meaningful comparison and monitoring of classification performance across test sets with differing imbalance rates. We illustrate how o-values can be applied to a variety of commonly used classification performance metrics and demonstrate the utility and robustness of our method through experiments on real-world datasets spanning multiple classification applications.

stat.ML↗

arXiv2024

Error Analysis of Shapley Value-Based Model Explanations: An Informative Perspective

Shapley value attribution (SVA) is an increasingly popular explainable AI (XAI) method, which quantifies the contribution of each feature to the model's output. However, recent work has shown that most existing methods to implement SVAs have some drawbacks, resulting in biased or unreliable explanations that fail to correctly capture the true intrinsic relationships between features and model outputs. Moreover, the mechanism and consequences of these drawbacks have not been discussed systematically. In this paper, we propose a novel error theoretical analysis framework, in which the explanation errors of SVAs are decomposed into two components: observation bias and structural bias. We further clarify the underlying causes of these two biases and demonstrate that there is a trade-off between them. Based on this error analysis framework, we develop two novel concepts: over-informative and underinformative explanations. We demonstrate how these concepts can be effectively used to understand potential errors of existing SVA methods. In particular, for the widely deployed assumption-based SVAs, we find that they can easily be under-informative due to the distribution drift caused by distributional assumptions. We propose a measurement tool to quantify such a distribution drift. Finally, our experiments illustrate how different existing SVA methods can be over- or under-informative. Our work sheds light on how errors incur in the estimation of SVAs and encourages new less error-prone methods.

cs.AI↗