Original Research: Is Using Fast-and-Frugal Trees Better Than Machine-Learning Trees?
In this series on Original Research, I will be sharing about my findings from some of the mini-projects that I have carried out on my own.
Fast-and-frugal trees (FFTs) are a specific type of classification decision tree with sequentially ordered cues, where every cue has two branches and one branch is an exit point (Martignon et al., 2003). The final cue in the sequence will have two exit points to ensure that a decision is always made. The whole point of using FFTs is to allow decisions to be optimised with as few cues as possible, especially when decision making is time-constrained and needs to be immediate. The figure below is a classic example of a ischemic heart disease decision tree trialed by Green & Mehr (1997).
Green & Mehr (1997) trialed the tree to help physicians determine the risk level of patients, to ensure that patients most likely suffering from acute ischemic heart disease get medical attention immediately, and that patients of low risk do not consume intensive care resources unnecessarily. They discovered that 3 cues were all it took to make sure a decision, based on the historical records…
