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进行训练，但它们的预测结果进一步用作至少一个额外层中一个或多个元模型的输入数据。构建模型堆叠可以产生高性能结果，但通常涉及试错过程。因此，我们先前开发的视觉分析系统StackGenVis主要通过评估预测性能来帮助用户选择一组高性能且多样化的模型。然而，该系统仅使用单一的逻辑回归元模型。在本文中，我们利用一种名为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。该交互式工具帮助用户根据预测概率和多种验证指标，以及预测特定问题数据实例的能力，直观地探索不同的单一元模型和元模型对。我们基于一个医学数据集的使用场景和专家访谈，对MetaStackVis进行了评估。