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  <subfield code="a">Wong_Leeds2020</subfield> 
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<subfield code="p">TRANSP-OR</subfield>
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<subfield code="a">TALK</subfield>
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  <subfield code="a">Wong, Melvin</subfield> 
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Demystifying out-of-sample discrete choice prediction: What can we learn from machine learning?</subfield>
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<subfield code="c">2020</subfield>
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CMC Online Seminar Series 2020</subfield>
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Choice Modelling Centre (CMC), University of Leeds, Leeds, UK</subfield>
<subfield code="d">November 17, 2020</subfield>
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<subfield code="a">
Out-of-sample (OOS) prediction is the cornerstone of both discrete choice and machine learning. However the two fields take very different approaches to improving model performance on unseen data. This seminar identifies the best practices, pitfalls and methodological developments of each field for OOS prediction, and how we can learn from each other.</subfield>
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