Abstract
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.
Published Version
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