Machine learning originated at the intersection of statistics, database science, and computer science. It is a powerful tool, capable of finding actionable insight in large quantities of data. Still, caution must be used in order to avoid common abuses of machine learning in the real world.

Conceptually, learning involves the abstraction of data into a structured representation, and the generalization of this structure into action that can be evaluated for utility. In practical terms, a machine learner uses data containing examples and features of the concept to be learned, and summarizes this data in the form of a model, which is then used for predictive or descriptive purposes. These purposes can be grouped into tasks, including classification, numeric prediction, pattern detection, and clustering. Among the many options, machine learning algorithms are chosen on the basis of the input data and the learning task.

R provides support for machine learning in the form of community-authored packages. These powerful tools are free to download, but need to be installed before they can be used. Each chapter in this book will introduce such packages as they are needed.

In the next chapter, we will further introduce the basic R commands that are used to manage and prepare data for machine learning. Though you might be tempted to skip this step and jump directly into thick of things, a common rule of thumb suggests that 80 percent or more of the time spent on typical machine learning projects is devoted to this step. As a result, investing in this early work will pay dividends later on.