Chapter 2. Linear Regression – The Blocking and Tackling of Machine Learning

It is important that we get started with a simple, yet extremely effective, technique that has been used for a long time: linear regression. Albert Einstein is believed to have remarked at one time or another that things should be made as simple as possible, but no simpler. This is sage advice and a good rule of thumb in the development of algorithms for machine learning. Considering the other techniques that we will discuss later, there is no simpler model than the tried and tested linear regression, which uses the least squares approach to predict a quantitative outcome. In fact, one could consider it to be the foundation of all the methods that we will discuss later, many of which are mere extensions. If you can master the linear regression method, well, then quite frankly, I believe you can master the rest of this book. Therefore, let us consider this a good point for starting start our journey towards becoming a machine-learning guru.

This chapter covers introductory material, and an expert in this subject can skip ahead to the next topic. Otherwise, ensure that you thoroughly understand this topic before venturing on to other, more complex learning methods. I believe you will discover that many of your projects can be addressed by just applying what is discussed in the following section. Linear regression is probably the easiest model to explain to your customers, most of whom will have at least a cursory understanding of R-squared. Many of them will have been exposed to it at great depth and thus, be comfortable with variable contribution, collinearity, and the like.