MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels

arXiv:2212.03539v3 Announce Type: replace
Abstract: Stacking (or stacked generalization) is an ensemble learning method with one main distinctiveness from the rest: even though several base models are trained on the original data set, their predictions are further used as input data for one or more metamodels arranged in at least one extra layer. Composing a stack of models can produce high-performance outcomes, but it usually involves a trial-and-error process. Therefore, our previously developed visual analytics system, StackGenVis, was mainly designed …

abstract arxiv cs.hc cs.lg data data set ensemble evaluation extra layer least performance predictions rest set stat.ml type