Smith, MatthewÁlvarez González, Francisco2023-06-172023-06-1720212665-963810.1016/j.simpa.2021.100074https://hdl.handle.net/20.500.14352/6773CRUE-CSIC (Acuerdos Transformativos 2021)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.engAtribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/journal articlehttps://doi.org/10.1016/j.simpa.2021.100074open accessMachine LearningBinary classificationCOVID19Shapley valuesInteligencia artificial (Informática)Lenguajes de programaciónTeorías económicas1203.04 Inteligencia Artificial1203.23 Lenguajes de Programación5307 Teoría Económica