Discrete choice modeling using Kernel Logistic Regression

sept. 18, 2019·
José Ángel Martín-Baos
Ricardo García-Ródenas
María Luz López-García
Luis Rodriguez-Benitez
· 1 min de lectura


The Kernel Logistic Regression is a popular technique in machine learning. In this work this tech- nique is applied to the field of discrete choice modeling. This approach is equivalent to specifying non-parametric utilities in random utility models. A Monte Carlo simulation experiment has been carried out to compare this approach with Multinomial Logit models, comparing the goodness of fit and the capability of obtaining the specified utilities.