REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values

arXiv:2403.08880v1 Announce Type: new
Abstract: Feature selection is a crucial step in building machine learning models. This process is often achieved with accuracy as an objective, and can be cumbersome and computationally expensive for large-scale datasets. Several additional model performance characteristics such as fairness and robustness are of importance for model development. As regulations are driving the need for more trustworthy models, deployed models need to be corrected for model characteristics associated with responsible artificial intelligence. When feature selection is …

abstract accuracy arxiv building cs.lg datasets fairness feature feature selection importance machine machine learning machine learning models performance process responsible robustness scale shap type values