Chapter 2. Managing and Understanding Data
A key early component of any machine learning project involves managing and understanding data. Although this may not be as gratifying as building and deploying models—the stages in which you begin to see the fruits of your labor—it is unwise to ignore this important preparatory work.
Any learning algorithm is only as good as its input data, and in many cases, the input data is complex, messy, and spread across multiple sources and formats. Because of this complexity, often the largest portion of effort invested in machine learning projects is spent on data preparation and exploration.
This chapter approaches these topics in three ways. The first section discusses the basic data structures R uses to store data. You will become very familiar with these structures as you create and manipulate datasets. The second section is practical, as it covers several functions that are useful to get data in and out of R. In the third section...
