In this chapter and the next, we will be solving one of the most important machine learning problems in digital online advertising, click-through prediction—given a user and the page they are visiting, how likely they will click on a given ad. We will be herein focusing on learning tree-based algorithms, decision tree and random forest, and utilizing them to tackle the billion dollar problem.

We will get into details for the topics mentioned:

• Introduction to online advertising click-through
• Two types of features, numerical and categorical
• Decision tree classifier
• The mechanics of decision tree
• The construction of decision tree
• The implementations of decision tree
• Click-through prediction with decision tree
• Random forest
• The mechanics of random forest
• Click-through prediction with random forest
• Tuning a random forest model