Last time we introduced decision trees and a tool we’ve made to explore them. With that tool, embedded in a simple game (Arbor), you can generate data from alien creatures with a simulated malady, figure out its predictors, and make a decision tree that will let you automate its diagnosis. (Here is the link to that not-quite-game.)
Your job was to get through the diseases ague and botulosis. Today I want to reflect on those two scenarios.
Ague is ridiculously simple, and with that ridiculous simplicity, the user is supposed to be able to learn the basics of the game, that is, how to “drive” the tools. One way to figure out the disease is to sort the table by health and see what matches health. Here is what the sorted table looks like:
Just scanning the various columns, you can see that health is associated with hair color. Pink means sick, blue means well. With that insight, you can go on to diagnose individual creatures and then make a simple tree, which looks like this:
Although there is a lot of information in the tree, users can generally figure it out. If they (or you) have trouble, they can get additional information by hovering over the boxes or the links.