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biogeme_tutorial's Introduction

6. Discrete Choice Models

This chapter explores how to estimate and analyze Discrete Choice Models, their uses and applications.

Goals:

  • Understand Biogeme concepts
  • Understand possible applications for Biogeme
  • Be able to create discrete choice models
  • Learn some tools that can be used to create more complicated models in the future
  • Learn some handy tricks/tools/packages

Biogeme Overview

Biogeme is a open source Python package designed for the maximum likelihood estimation of parametric models in general, with a special emphasis on discrete choice models.

--https://biogeme.epfl.ch/index.html

Summary

File Topic Description
01-biogeme-basics.ipynb Logit Basics How to estimate a simple multinomial logit model.
02-logit-estimators-results.ipynb Goodness of fit How to analyze the models goodness of fit indicators.
03-logit-validation.ipynb Cross-validation How to perform cross-validation on a dataset using a logit model.
04-logit-train-test.ipynb Prediction How to split a dataset into train and test dataset to perform prediction. Evaluate results on a confusion matrix.
05-probit-basics.ipynb Probit How to train a binomial probit model.
File Topic Description
01-logit-nested.ipynb Nested Logit Basics How to estimate a simple nested logit model.
02-logit-nested-market.ipynb Market Share How to compute market shares of alternatives.
03-logit-nested-revenue-scenarios.ipynb Market Share Scenarios How to create scenarios and compute their market share outcomes.
04-logit-nested-elasticities.ipynb Elasticities How to compute elasticities.
05-logit-nested-WTP.ipynb Willingness To Pay (WTP) How to compute willingness to pay indicators.
File Topic Description
01-biogeme-ordered.ipynb Ordered Logit Basics How to estimate a simple ordered logit model.

Other Useful Links

Python and Biogeme instalation

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