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Stacking (or stacked generalization) is an ensemble learning method with onemain distinctiveness from the rest: even though several base models are trainedon the original data set, their predictions are further used as input data forone or more metamodels arranged in at least one extra layer. Composing a stackof models can produce high-performance outcomes, but it usually involves atrial-and-error process. Therefore, our previously developed visual analyticssystem, StackGenVis, was mainly designed to assist users in choosing a set oftop-performing and diverse models by measuring their predictive performance.However, it only employs a single logistic regression metamodel. In this paper,we investigate the impact of alternative metamodels on the performance ofstacking ensembles using a novel visualization tool, called MetaStackVis. Ourinteractive tool helps users to visually explore different singular and pairsof metamodels according to their predictive probabilities and multiplevalidation metrics, as well as their ability to predict specific problematicdata instances. MetaStackVis was evaluated with a usage scenario based on amedical data set and via expert interviews.

MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels