Speaker
Description
Real world datasets frequently include not only vast numbers of observations but also high dimensional feature spaces. Exhaustively gathering and examining every variable to uncover meaningful insights can be time consuming, costly, or even infeasible. In order to build up robust, reliable and efficient regression models, feature selection techniques have therefore become inevitable. Yet many established methods consider only a single partition of the training data, risking biased or sub optimal variable choices. This work introduces a modified forward selection strategy for feature selection executed over multiple data splits, accounting for inter variable relationships and structural variation within the data to highlight the most influential variables.
| Classification | Both methodology and application |
|---|---|
| Keywords | Machine Learning, Feature Selection, Multicollinearity |