Lecture
Discrete choice and machine learning: two complementary methodologies
Description
This lecture covers the concepts of discrete choice and machine learning as two complementary methodologies. It explains supervised learning in machine learning, the advantages of machine learning over discrete choice in model specification, development, and selection, as well as the pitfalls of machine learning for choice data. The lecture also discusses aggregation bias in machine learning, probabilistic classification methods for choice data, and the importance of panel data and out-of-sample validation.
Official source
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