Low-use tangible print collections represent a long-standing problem for academic libraries. Expanding on the previous research aimed at leveraging machine learning (ML) toward predicting patterns of collection use, this study explores the potential for adaptive boosting (AdaBoost) as a foundation for developing actionable predictive models of print title use. This study deploys the AdaBoost algorithm, with random forests used as the base classifier, via the adabag package for R. Methodological considerations associated with dataset congruence, as well as sample-based modeling versus novel data modeling, are explored in relation to four AdaBoost models that are trained and tested. Results of this study show AdaBoost as a promising ML solution for predictive modeling of print collections, with the central model of interest able to accurately predict use in over 85% of cases. This research also explores peripheral questions of interest related to general considerations when evaluating ML models, as well as the compatibility of similar models trained with e-book versus print book usage data.

低使用量的有形印刷馆藏是学术图书馆长期存在的问题。在之前旨在利用机器学习 (ML) 预测馆藏使用模式的研究的基础上，本研究探索了自适应增强 (AdaBoost) 作为开发印刷标题使用的可操作预测模型的基础的潜力。本研究部署了 AdaBoost 算法，将随机森林用作基本分类器，通过R的adabag包。与数据集一致性相关的方法论考虑，以及基于样本的建模与新的数据建模, 与四个经过训练和测试的 AdaBoost 模型相关。这项研究的结果表明，AdaBoost 是一种很有前途的 ML 解决方案，可用于印刷品集合的预测建模，感兴趣的中心模型能够准确预测超过 85% 的使用情况。这项研究还探讨了与评估 ML 模型时的一般考虑相关的感兴趣的外围问题，以及使用电子书与印刷书籍使用数据训练的类似模型的兼容性。