After reading this chapter, you should now know the base techniques that are used to win data mining and machine learning competitions. Automated tuning methods can assist with squeezing every bit of performance out of a single model. On the other hand, performance gains are also possible by creating groups of machine learning models that work together.

Although this chapter was designed to help you prepare competition-ready models, note that your fellow competitors have access to the same techniques. You won't be able to get away with stagnancy; therefore, continue to add proprietary methods to your bag of tricks. Perhaps you can bring unique subject-matter expertise to the table, or perhaps your strengths include an eye for detail in data preparation. In any case, practice makes perfect, so take advantage of open competitions to test, evaluate, and improve your own machine learning skillset.

In the next chapter—the last in this book—we'll take a bird's-eye look at ways to apply machine learning to some highly specialized and difficult domains using R. You'll gain the knowledge needed to apply machine learning to tasks at the cutting edge of the field, involving extremely large, challenging, or unusual datasets.