Dissecting Moneyball: Improving Classification Model Interpretability in Baseball Pitch Prediction

dc.contributor.author Hickey, Kevin
dc.contributor.author Zhou, Lina
dc.contributor.author Tao, Jie
dc.date.accessioned 2020-01-04T07:11:08Z
dc.date.available 2020-01-04T07:11:08Z
dc.date.issued 2020-01-07
dc.description.abstract Data science, where technical expertise meets do-main knowledge, is collaborative by nature. Complex machine learning models have achieved human-level performance in many areas, yet they face adoption challenges in practice due to limited interpretability of model outputs, particularly for users who lack specialized technical knowledge. One key question is how to unpack complex classification models by enhancing their interpretability to facilitate collaboration in data science research and application. In this study, we extend two state-of-the-art methods for drawing fine-grained explanations from the results of classification models. The main extensions include aggregating explanations from individual instances to a user-defined aggregation level, and providing explanations with the original features rather than engineered representations. We use the prediction of baseball pitch outcome as a case to evaluate our extended methods. The experiment results of the methods with real sensor data demonstrate their improved interpretability while pre-serving superior prediction performance.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.031
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63770
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Collaboration for Data Science
dc.subject baseball analytics
dc.subject data science
dc.subject machine learning
dc.subject model interpretability
dc.subject predictive analysis
dc.title Dissecting Moneyball: Improving Classification Model Interpretability in Baseball Pitch Prediction
dc.type Conference Paper
dc.type.dcmi Text
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