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 to assist users in choosing a set of top-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 of stacking ensembles using a novel visualization tool, called MetaStackVis. Our interactive tool helps users to visually explore different singular and pairs of metamodels according to their predictive probabilities and multiple validation metrics, as well as their ability to predict specific problematic data instances. MetaStackVis was evaluated with a usage scenario based on a medical data set and via expert interviews.

翻译：堆叠（或称堆叠泛化）是一种集成学习方法，其与其它方法的主要区别在于：尽管多个基模型在原始数据集上训练，但其预测结果会进一步用作至少一个额外层中排列的一个或多个元模型的输入数据。组合模型堆叠可以产生高性能结果，但通常涉及反复试错的过程。因此，我们先前开发的视觉分析系统 StackGenVis 主要用于通过测量预测性能，帮助用户选择一组性能优异且多样化的模型。然而，该系统仅采用单一的逻辑回归元模型。本文中，我们使用一种名为 MetaStackVis 的新型可视化工具，研究替代元模型对堆叠集成性能的影响。我们的交互式工具能够帮助用户根据预测概率、多种验证指标以及预测特定困难数据实例的能力，直观地探索不同的单一元模型和元模型对。MetaStackVis 通过基于医学数据集的使用场景及专家访谈进行了评估。