Package: gingado
Welcome to gingado!gingadoseeks to facilitate the use of machine learning in economic
and finance use cases, while promoting good practices. This package aims
to be suitable for beginners and advanced users alike. Use cases may
range from simple data retrievals to experimentation with machine
learning algorithms to more complex model pipelines used in production.Overviewgingadois a free, open source library built different
functionalities:data
augmentation,
to add data from official sources, improving the machine models being
trained by the user;relevantdatasets,
both real and simulated, to allow for easier model development and
comparison;automaticbenchmark
model, to
assess candidate models against a reasonably well-performant model;machine learning-basedestimators,
to help answer questions of academic or practical importance;support formodel
documentation,
to embed documentation and ethical considerations in the model
development phase; andutilities,
including tools to allow for lagging variables in a straightforward
way.Each of these functionalities builds on top of the previous one. They
can be used on a stand-alone basis, together, or even as part of a
larger pipeline from data input to model training to documentation!TipNew functionalities are planned over time, so consider checking
frequently ongingadofor the latest toolsets.InstallNotePlease make sure you have read and understood the license disclaimer
in the NOTES.md file in ourGitHub
repositorybefore using
gingado.To installgingado, simply run the following code on the terminal:$ pip install gingadoAttributionIf you use this package in your work, please consider citing Araujo
(2023).In BibTeX format:@techreport{gingado,
    author = {Araujo, Douglas KG},
    title = {gingado: a machine learning library focused on economics and finance},
    series = {BIS Working Paper},
    type = {Working Paper},
    institution = {Bank for International Settlements},
    year = {2023},
    number = {1122}
}Over time, new tools that are described in specific papers might be
added (eg, a machine learning-based econometric estimator). Please
consider citing them as well if used in your work. Specific information,
if any, can be found in the documentation.Design principlesThe choices made during development ofgingadoderive from the
following principles, in no particular order:flexibility: users can usegingadoout of the box or build
custom processes on top of it;compatibility:gingadoworks well with other widely used
libraries in machine learning, such asscikit-learnandpandas;
andresponsibility:gingadofacilitates and promotes model
documentation, including ethical considerations, as part of the
machine learning development workflow.For more information aboutgingado, please read thepaper.Acknowledgementsgingado’s API is inspired on the following libraries:scikit-learn(Buitinck et al. 2013)keras(websitehereand also,this
essay)fastai(Howard and Gugger 2020)In addition,gingadois developed and maintained usingquarto.ReferencesAraujo, Douglas KG. 2023. “Gingado: A Machine Learning Library Focused
on Economics and Finance.” Working Paper 1122. BIS Working Paper. Bank
for International Settlements.Buitinck, Lars, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa,
Andreas Mueller, Olivier Grisel, Vlad Niculae, et al. 2013. “API Design
for Machine Learning Software: Experiences from the Scikit-Learn
Project.”CoRRabs/1309.0238.http://arxiv.org/abs/1309.0238.Howard, Jeremy, and Sylvain Gugger. 2020. “Fastai: A Layered API for
Deep Learning.”Information11 (2).https://doi.org/10.3390/info11020108.