Predicting Online Ad Click-Through with Tree-Based Algorithms

We built a face image classifier in the previous chapter. In this chapter and the next, we will be solving one of the most data-driven problems in digital advertising: ad click-through prediction—given a user and the page they are visiting, this predicts how likely it is that they will click on a given ad. We will focus on learning tree-based algorithms (including decision tree, random forest, and boosted trees) and utilize them to tackle this billion-dollar problem. We will be exploring decision trees from the root to the leaves, as well as the aggregated version, a forest of trees. This won't be a theory-only chapter, as there are a lot of hand calculations and implementations ...

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