A case study in discrete choice model assessment with predictive simulations.
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models
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reports
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LICENSE
Makefile
README.md
requirements.txt
setup.py
test_environment.py
tox.ini

README.md

Check Yourself Before You Wreck Yourself: Assessing Discrete Choice Models Through Predictive Simulations

A case study in discrete choice model assessment with predictive simulations.

This repository contains the replication data and code for

Brathwaite, Timothy. "Check yourself before you wreck yourself: Assessing
discrete choice models through predictive simulations" arXiv preprint
arXiv:1806.02307 (2018). https://arxiv.org/abs/1806.02307.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or
│                         `make train`
│
├── README.md          <- The top-level README for developers using this
│                         project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for
│                         details
│
├── models             <- Trained and serialized models, model predictions,
│                         or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number
│                         (for ordering), the creator's initials, and a
│                         short `-` delimited description, e.g.
│                         `_01-jqp-initial-data-exploration`
│
├── references         <- Data dictionaries, manuals, key reference papers,
│                         and all other explanatory materials
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in
│                         reporting
│   └── tables         <- LaTex files for tables to be used in reporting
│   └── complete       <- LaTex files for the final report and journal
│                         submission
│
├── requirements.txt   <- The requirements file to reproduce the analysis
│                         environment, e.g. generated with
│                         `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .)
│                         so src can be imported
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   ├── get_car_data.R
│   │   └── convert_car_data_from_wide_to_long.py
│   │
│   ├── features       <- Scripts to turn raw data into modeling features
│   │   ├── build_features.py
│   │   └── build_forecast_data.py
│   │
│   ├── models         <- Scripts to train models and then use trained
│   │   │                 models to make predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results
│                         oriented visualizations
│       └── predictive_viz.py
│
└── tox.ini            <- tox settings file; see tox.testrun.org

Project based on the cookiecutter data science project template. #cookiecutterdatascience