RT Journal Article
T1 A machine learning research template for binary classification problems and shapley values integration
A1 Smith, Matthew
A1 Álvarez González, Francisco
AB 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.
PB Elsevier
SN 2665-9638
YR 2021
FD 2021
LK https://hdl.handle.net/20.500.14352/6773
UL https://hdl.handle.net/20.500.14352/6773
LA eng
NO CRUE-CSIC (Acuerdos Transformativos 2021)
DS Docta Complutense
RD 4 abr 2025