Outperformance Score: A Universal Standardization Method 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 interpret and evaluate classification performances, especially when imbalance rates vary. To address this problem, we introduce the outperformance score 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 outperformance score 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 the outperformance scores can be applied to a variety of commonly used classification performance metrics and demonstrate the robustness of our method through experiments on real-world datasets spanning multiple classification applications.

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

Shapley value attribution 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 Shapley value attributions have some drawbacks. Due to these drawbacks, the resulting Shapley value attributions may provide biased or unreliable explanations, which fail to correctly capture the true intrinsic relationships between features and model outputs. Moreover, it is difficult to evaluate these explanation errors because the true underlying dependencies between features and model outputs are typically unknown. In this paper, we theoretically analyze the explanation errors of Shapley value attributions by decomposing the explanation error into two components: observation bias and structural bias. We also 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 under-informative explanations. We theoretically analyze the potential over-informativeness and under-informativeness of existing Shapley value attribution methods. Particularly for the widely deployed assumption-based Shapley value attributions, we affirm that they can easily be under-informative due to the distribution drift caused by distributional assumptions. We also propose a measurement tool to quantify the distribution drift that causes such errors.