Note to self… need to do more data analysis with decision trees. Besides this bigML article, I recently saw a great presentation at a meetup that reminded me of what a great job decision trees do for analyzing features.
If you’ve built decision trees with BigML or explored our gallery, then you should be familiar with our tree visualizations. They’re a classic and intuitive way to view trees. The root is at the top, its children are the next level down, the grandchildren are deeper still, and so forth.
While intuitive, this sort of visualization does have some drawbacks. Decision trees often grow too wide to comfortably fit the display area. We compensate by collapsing the less important parts of the tree and then letting the user choose where to drill down (either picking specific branches or with our filtering options). It works, and we’re happy with it as our default visualization. But it’s not the only way to look at a decision tree.
Recently we’ve explored SunBurst tree visualizations as a complement to our current approach. A SunBurst diagram is a little like nested pie charts. Instead of…
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