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Decision trees

Finally, we arrive at our last model in this chapter: decision trees. Similar to linear models, decision trees are spread out and good—although not as easy to digest—for interpretation. The core idea behind trees is very different from linear models but is easy to comprehend. To estimate the outcome, the model generates a binary tree—a tree-like diagram—where each note (intersection) represents a single question, based on the known features, with a yes/no answer. Usually, it is something like the number of casualties is smaller than 1,000. At the end of each branch, a corresponding estimate is attached. The tree is generated so that the average accuracy of predictions is maximized.

As the depth of the tree can vary, decision ...