Stacking, or stacked generalization, is a technique in ensemble learning where multiple base models, or "weak learners," are trained and combined to form a metamodel with improved predictive power. The ISOVIS research group at LNU has created StackGenVis, a visual analytics system that helps users optimize performance metrics, manage input data, including selecting features, and choose top-performing algorithms. The current version of StackGenVis uses a single Linear Regression metamodel. This work aims to investigate the impact of alternative metamodels on the predictive performance of StackGenVis using provided data and charts for comparison.
This work is based on the original work by Angelos Chatzimparmpas, available here:
StackGenVis
Angelos Chatzimparmpas and Prof. Dr. Andreas Kerren
Linnaeus university, Faculty of Technology