The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.

中文翻译：

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一种预测信用风险违约的新模型平均方法

本文介绍了一种作为加权模型平均技术的集成建模的新方法。与贝叶斯和频率论方法相比，所提出的想法谨慎、易于理解且易于实施。本文通过创建集成模型作为机器学习模型的加权线性组合，为以新方式有效评估信用风险（违约概率）提供了理论和经验贡献。这个想法可以推广到其他领域中的任何分类问题，其中集成类型建模是一个感兴趣的主题，不限于不平衡的数据集或信用风险评估。结果表明，与参数、非参数和其他集成模型的单一最著名的机器学习相比，它具有更好的预测性能。