Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection
Description
In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to
