Abstract

We introduce a pair of Shiny web applications that allow users to visualize random forest prediction intervals alongside those produced by linear regression models. The apps are designed to help undergraduate students deepen their understanding of the role that assumptions play in statistical modeling by comparing and contrasting intervals produced by regression models with those produced by more flexible algorithmic techniques. We describe the mechanics of each approach, illustrate the features of the apps, provide examples highlighting the insights students can gain through their use, and discuss our experience implementing them in an undergraduate class. We argue that, contrary to their reputation as a black box, random forests can be used as a spotlight, for educational purposes, illuminating the role of assumptions in regression models and their impact on the shape, width, and coverage rates of prediction intervals.

摘要

我们引入了一对闪亮的网络应用程序，允许用户将随机森林预测区间与线性回归模型产生的区间一起可视化。这些应用程序旨在通过比较和对比回归模型产生的区间与更灵活的算法技术产生的区间，帮助本科生加深对假设在统计建模中的作用的理解。我们描述了每种方法的机制，说明了应用程序的功能，提供了突出学生可以通过使用它们获得的见解的示例，并讨论了我们在本科课程中实施它们的经验。我们认为，与它们作为黑匣子的名声相反，随机森林可以用作聚光灯，用于教育目的，