Scalable Visualization Methods for Modern Generalized Additive Models
Scalable Visualization Methods for Modern Generalized Additive Models
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DOI：
10.1080/10618600.2019.1629942
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发表时间：
2019-07-19
影响因子：
2.4
通讯作者：
Wood, Simon N.
中科院分区：
文献类型：
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作者：
Fasiolo, Matteo;Nedellec, Raphael;Wood, Simon N.
在过去的二十年里，计算资源的增长使得处理广义加性模型（GAM）成为可能，而这些模型以前对于严肃的应用来说过于昂贵。然而，模型复杂性的增长并没有与模型开发和结果呈现的改进可视化相匹配。受电力负荷预测中的工业应用的启发，我们确定了缺乏用于GAM的现代可视化工具的领域特别严重，并且我们通过提出一组可视化工具来解决现有方法的缺点，所述可视化工具（a）对于交互式使用足够快，（B）利用GAM的添加剂结构，（c）扩展到大数据集，以及（d）可以与宽范围的响应分布结合使用。这里提出的新视觉方法由mgcViz R包实现，可在Comprehensive R Archive Network上获得。可以在网上找到。
In the last two decades, the growth of computational resources has made it possible to handle generalized additive models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualizations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify the areas where the lack of modern visualization tools for GAMs is particularly severe, and we address the shortcomings of existing methods by proposing a set of visual tools that (a) are fast enough for interactive use, (b) exploit the additive structure of GAMs, (c) scale to large data sets, and (d) can be used in conjunction with a wide range of response distributions. The new visual methods proposed here are implemented by the mgcViz R package, available on the Comprehensive R Archive Network. for this article are available online.