突破可解释性边界：可解释提升机的逐步增强
Pushing the Boundaries of Interpretability: Incremental Enhancements to the Explainable Boosting Machine
AI总结：
本文通过改进可解释提升机，提出三种方法提升模型的透明性和公平性，以实现更稳健和公正的机器学习系统。
摘要：
复杂机器学习模型在高风险领域中的广泛应用，使
英文摘要：
The widespread adoption of complex machine learning models in high-stakes domains has brought the "black-box" problem to the forefront of responsible AI research. This paper aims at addressing this issue by improving the Explainable Boosting Machine (EBM), a state-of-the-art glassbox model that delivers both high accuracy and complete transparency. The paper outlines three distinct enhancement methodologies: targeted hyperparameter optimization with Bayesian methods, the implementation of a custom multi-objective function for fairness for hyperparameter optimization, and a novel self-supervised pre-training pipeline for cold-start scenarios. All three methodologies are evaluated across standard benchmark datasets, including the Adult Income, Credit Card Fraud Detection, and UCI Heart Disease datasets. The analysis indicates that while the tuning process yielded marginal improvements in the primary ROC AUC metric, it led to a subtle but important shift in the model's decision-making behavior, demonstrating the value of a multi-faceted evaluation beyond a single performance score. This work is positioned as a critical step toward developing machine learning systems that are not only accurate but also robust, equitable, and transparent, meeting the growing demands of regulatory and ethical compliance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
