A machine learning research template for binary classification problems and shapley values integration

Cita com:
hdl:2117/345914
Document typeArticle
Defense date2021-05
PublisherElsevier
Rights accessOpen Access
Except where otherwise noted, content on this work
is licensed under a Creative Commons license
:
Attribution 3.0 Spain
Abstract
This paper documents published code which can help facilitate researchers with binary classification problems and interpret the results from a number of Machine Learning models. The original paper was published in Expert Systems with Applications and this paper documents the code and work-flow with a special interest being paid to Shapley values as a means to interpret Machine Learning predictions. The Machine Learning models used are, Naive Bayes, Logistic Regression, Random Forest, adaBoost, Classification Tree, Light GBM and XGBoost.
CitationSmith, M.; Alvarez, F. A machine learning research template for binary classification problems and shapley values integration. "Software Impacts", Maig 2021, vol. 8, 100074.
ISSN2665-9638
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S2665963821000221
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