R: Unleash Machine Learning Techniques

Summary

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 would cost the bank more money.

We also used two rule learners, 1R and RIPPER, ...