@techreport{EngelErickson2023,
  type      = {Working Paper},
  author    = {Joachim Engel and Tim Erickson},
  title     = {What goes before the CART? Introducing classification trees with Arbor and CODAP},
  number    = {TEACHING STATISTICS 45, S1},
  institution = {Wiley Online Library},
  doi       = {10.1111/test.12347},
  year      = {2023},
  abstract  = {This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build-it-yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree-building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction.},
  language  = {en}
}