Conservative Decisions with Risk Scores
提出一种确定风险评分最优截断区间的方法，在区间内算法放弃决策以最大化区间外的分类准确率，并生成风险覆盖曲线作为分类器性能指标，通过模拟和前列腺癌诊断案例验证。
In binary classification applications, conservative decision-making that allows for abstention can be advantageous. To this end, we introduce a novel approach that determines the optimal cutoff interval for risk scores, which can be directly available or derived from fitted models. Within this interval, the algorithm refrains from making decisions, while outside the interval, classification accuracy is maximized. Our approach is inspired by support vector machines (SVM), but differs in that it minimizes the classification margin rather than maximizing it. We provide the theoretical optimal solution to this problem, which holds important practical implications. Our proposed method not only supports conservative decision-making but also inherently results in a risk-coverage curve. Together with its associated area under the curve, this representation offers a comprehensive performance metric for evaluating and comparing classifiers, akin to the receiver operating characteristic (ROC). To investigate and illustrate our approach, we conduct both simulation studies and a real-world case study in the context of diagnosing prostate cancer. Supplemental materials are available online.