Dissecting Moneyball: Improving Classification Model Interpretability in Baseball Pitch Prediction

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dc.contributor.authorHickey, Kevin
dc.contributor.authorZhou, Lina
dc.contributor.authorTao, Jie
dc.date.accessioned2020-01-04T07:11:08Z
dc.date.available2020-01-04T07:11:08Z
dc.date.issued2020-01-07
dc.description.abstractData 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.031
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63770
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCollaboration for Data Science
dc.subjectbaseball analytics
dc.subjectdata science
dc.subjectmachine learning
dc.subjectmodel interpretability
dc.subjectpredictive analysis
dc.titleDissecting Moneyball: Improving Classification Model Interpretability in Baseball Pitch Prediction
dc.typeConference Paper
dc.type.dcmiText

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