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

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10.1016/j.simpa.2021.100074
 
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Smith, Matthew
Alvarez, Francisco
Document typeArticle
Defense date2021-05
PublisherElsevier
Rights accessOpen Access
Attribution 3.0 Spain
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. 
URIhttp://hdl.handle.net/2117/345914
DOI10.1016/j.simpa.2021.100074
ISSN2665-9638
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S2665963821000221
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  • Life Sciences - Articles de revista [308]
  • COVID-19 - Col·lecció especial COVID-19 [564]
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