3 ms·

Single decision trees? Or forests?

• mywittyname 6y ago
  In most of my work, I could get away with just one big decision tree. I was analyzing user traffic patterns, which has a limited set of metrics associated with it (and lots of boolean values). I wrote a tool that would go through and find "representative examples" of sessions for each tree path, then I could sit down with the UX designers and show them exactly what the user was doing. This was helpful as that group was the most vocal critics of our work, mainly because the data we provided began to drive UX design from senior management, and they weren't happy about it. But when I could sit down and show them exactly how the user was spinning their wheels, they were more likely to buy into changes (and offer effective solutions). Plus, I could inform the UX team which design detail was the primary driver for some event we were testing for. An example would be if the user was applying more than three filters, they were less likely to complete their goal. Thus, the UX team was able to devise a couple of reworks to the filtering mechanisms, which improved customer feedback. This tactic would probably be much more difficult if we used NN to solve the same problem.