A Computational Exploration of Emerging Methods of Variable Importance Estimation. (arXiv:2208.03373v1 [stat.ML])

Estimating the importance of variables is an essential task in modern machine
learning. This help to evaluate the goodness of a feature in a given model.
Several techniques for estimating the importance of variables have been
developed during the last decade. In this paper, we proposed a computational
and theoretical exploration of the emerging methods of variable importance
estimation, namely: Least Absolute Shrinkage and Selection Operator (LASSO),
Support Vector Machine (SVM), the Predictive Error Function (PERF), Random
Forest (RF), and …

arxiv computational exploration importance ml