Cracking the black box: Distilling deep sports analytics
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics. Neural nets achieve great predictive accuracy through deep learning, and are popular in sports analytics. But it is hard to interpret a neural net model and harder still to extract actionable insights from the knowledge implicit in it. Therefore, we built a simple and transparent model that mimics the output of the original deep learning model and represents the learned knowledge in an explicit interpretable way. Our mimic model is a linear model tree, which combines a collection of linear models with a regression-tree structure. The tree version of a neural network achieves high fidelity, explains itself, and produces insights for expert stakeholders such as athletes and coaches.
© Copyright 2020 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020). Julkaistu Tekijä Association for Computing Machinery. Kaikki oikeudet pidätetään.
Aiheet: | harjoittelu kilpailu suorituskyky rakenne suoritusrakenne mallintaminen ohjelmisto menetelmä jääkiekko analyysi |
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Aihealueet: | tekniset ja luonnontieteet urheilukilpailut |
Tagging: | deep learning neuronale Netze |
DOI: | 10.1145/3394486.3403367 |
Julkaisussa: | 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020) |
Toimittajat: | R. Gupta, Y. Liu |
Julkaistu: |
New York
Association for Computing Machinery
2020
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Sivuja: | 3154-3162 |
Julkaisutyypit: | artikkeli |
Kieli: | englanti (kieli) |
Taso: | kehittynyt |