Das Subhajit, Cashman Dylan, Chang Remco, Endert Alex
IEEE Comput Graph Appl. 2019 Sep-Oct;39(5):20-32. doi: 10.1109/MCG.2019.2922592. Epub 2019 Jun 12.
Interactive model steering helps people incrementally build machine learning models that are tailored to their domain and task. Existing visual analytic tools allow people to steer a single model (e.g., assignment attribute weights used by a dimension reduction model). However, the choice of model is critical in such situations. What if the model chosen is suboptimal for the task, dataset, or question being asked? What if instead of parameterizing and steering this model, a different model provides a better fit? This paper presents a technique to allow users to inspect and steer multiple machine learning models. The technique steers and samples models from a broader set of learning algorithms and model types. We incorporate this technique into a visual analytic prototype, BEAMES, that allows users to perform regression tasks via multimodel steering. This paper demonstrates the effectiveness of BEAMES via a use case, and discusses broader implications for multimodel steering.
交互式模型引导有助于人们逐步构建适合其领域和任务的机器学习模型。现有的可视化分析工具允许人们引导单个模型（例如，降维模型使用的分配属性权重）。然而，在这种情况下，模型的选择至关重要。如果所选模型对于任务、数据集或所提问题不是最优的会怎样？如果不是对这个模型进行参数化和引导，而是一个不同的模型能提供更好的拟合又会怎样？本文提出了一种允许用户检查和引导多个机器学习模型的技术。该技术从更广泛的学习算法和模型类型集合中引导和采样模型。我们将此技术整合到一个可视化分析原型BEAMES中，该原型允许用户通过多模型引导执行回归任务。本文通过一个用例展示了BEAMES的有效性，并讨论了多模型引导的更广泛影响。
