Logistic包围模型
Logistic Ensemble Models
摘要：
在受监管的行业或受监管的应用中开发的预测模型，如信用价值的确定，必须是可解释的和合理的（例如，基本信用行为的有意义的改进必须导致改进的信用价值分数）。机器学习技术提供了非常好的性能，分析人员的干预最少，使它们非常适合大容量的分析环境，但大多数是黑盒工具，提供非常有限的洞察力或解释模型性能或预测模型输出值的关键驱动因素。本文提出了一种方法，将最流行的二进制分类预测统计建模方法之一与机器学习中的核心模型增强策略相结合。由此产生的预测方法提供了可靠的性能，从最小的分析工作，同时提供所需的可解释性和合理性，在受监管的行业，以及在其他环境中，需要解释模型参数（例如，需要解释模型的企业，采取行动）。
英文摘要：
Predictive models that are developed in a regulated industry or a regulated application, like determination of credit worthiness, must be interpretable and rational (e.g., meaningful improvements in basic credit behavior must result in improved credit worthiness scores). Machine Learning technologies provide very good performance with minimal analyst intervention, making them well suited to a high volume analytic environment, but the majority are black box tools that provide very limited insight or interpretability into key drivers of model performance or predicted model output values. This paper presents a methodology that blends one of the most popular predictive statistical modeling methods for binary classification with a core model enhancement strategy found in machine learning. The resulting prediction methodology provides solid performance, from minimal analyst effort, while providing the interpretability and rationality required in regulated industries, as well as in other environments where interpretation of model parameters is required (e.g. businesses that require interpretation of models, to take action on them).
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。
