This chapter covered two classification methods that use so-called "greedy" algorithms to partition the data according to feature values. Decision trees use a divide and conquer strategy to create flowchart-like structures, while rule learners separate and conquer data to identify logical if-else rules. Both methods produce models that can be interpreted without a statistical background.

One popular and highly configurable decision tree algorithm is C5.0. We used the C5.0 algorithm to create a tree to predict whether a loan applicant will default. Using options for boosting and cost-sensitive errors, we were able to improve our accuracy and avoid risky loans that could cost the bank more money.

We also used two rule learners, 1R and RIPPER, to develop rules for identifying poisonous mushrooms. The 1R algorithm used a single feature to achieve 99 percent accuracy in identifying potentially fatal mushroom samples. On the other hand, the set of eight rules generated by the more sophisticated...