Machine Learning in Python Essential Techniques for Predictive Analysis by Michael Bowles (z-lib.org).epub

CHAPTER 2
Understand the Problem by
Understanding the Data
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 Anatomy of a New Problem
The algorithms covered in this book start with a matrix (or table) full
of numbers and perhaps some character variables. The example in
Table 2.1 establishes some nomenclature and represents a small