A new data set (problem) is a wrapped gift. It’s full of promise and anticipation at the miracles you can wreak once you’ve solved it. But it remains a mystery until you’ve opened it. This chapter is about opening up your new data set so you can see what’s inside, get an appreciation for what you’ll be able to do with the data, and start thinking about how you’ll approach model building with it.
This chapter has two purposes. One is to familiarize you with data sets that will be used later as examples of different types of problems to be solved using the algorithms you’ll learn in Chapter 4, “Penalized Linear Regression,” and Chapter 6, “Ensemble Methods.” The other purpose is to demonstrate some of the tools available in Python for data exploration.
The chapter uses a simple example to review some basic problem structure, nomenclature, and characteristics of a machine learning data set. The language introduced in this section will be used throughout the rest of the book. After establishing some common language, the chapter goes one by one through several different types of function approximation problems. These problems illustrate common variations of machine learning problems so that you’ll know how to recognize the variants when you see them and will know how to handle them (and will have code examples for them).
The algorithms covered in this book start with a matrix (or table) full ...