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

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Smith, Matthew and Álvarez González, Francisco (2021) A machine learning research template for binary classification problems and shapley values integration. Software Impacts, 8 . p. 100074. ISSN 2665-9638

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Official URL: https://doi.org/10.1016/j.simpa.2021.100074



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.


Item Type:Article
Additional Information:

CRUE-CSIC (Acuerdos Transformativos 2021)

Uncontrolled Keywords:Machine Learning, Binary classification, COVID19, Shapley values
Subjects:Sciences > Computer science > Artificial intelligence
Sciences > Computer science > Programming languages (Electronic computers)
Social sciences > Economics > Economics
ID Code:70260
Deposited On:09 Feb 2022 16:50
Last Modified:18 Oct 2022 09:44

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