MLU-expl AI n Decision Trees
The unreasonable power of nested decision rules. By Jared Wilber & Lucía Santamaría
Let's Build a Decision Tree Let's pretend we're farmers with a new plot of land. Given only the Diameter and Height of a tree trunk, we must determine if it's an Apple, Cherry, or Oak tree. To do this, we'll use a Decision Tree. Start Splitting Almost every tree with a Diameter ≥ 0.45 is an Oak tree! Thus, we can probably assume that any other trees we find in that region will also be one.
This first decision node will act as our root node. We'll draw a vertical line at this Diameter and classify everything above it as Oak (our first leaf node), and continue to partition our remaining data on the left. Split Some More We continue along, hoping to split our plot of land in the most favorable manner. We see that creating a new decision node at Height ≤ 4.88 leads to a nice section of Cherry trees, so we partition our data there.
Our Decision Tree updates accordingly, adding a new leaf node for Cherry. And Some More After this second split we're left with an area containing many Apple and some Cherry trees. No problem: a vertical division can be drawn to separate the Apple trees a bit better.
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