In this chapter, we will look at predicting the winner of sports matches using a different type of classification algorithm: decision trees. These algorithms have a number of advantages over other algorithms. One of the main advantages is that they are readable by humans. In this way, decision trees can be used to learn a procedure, which could then be given to a human to perform if needed. Another advantage is that they work with a variety of features, which we will see in this chapter.
We will cover the following topics in this chapter:
Using the pandas library for loading and manipulating data
Decision trees
Random forests
Using real-world datasets in data mining
Creating new features and testing them in a robust framework