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In Chapter 4, we briefly spoke about linear regression in the context of hyperparameter optimization, but now we will take a closer look at it and some of its derivatives. Linear regression is probably one of the first data models we encounter in our formal school days. You may not have realized it at the time (and I won’t hold it against you since depending how long ago you were in high school), but linear regression is really a type of simple ML model that attempts to find a mathematical relationship between data points that can be represented by the equation for a line. The most straightforward approach (pun somewhat intended) is a straight line like that in Ordinary Least Squares (OLS) regression, but as we move along in the chapter, we’ll begin to see that there are a variety of methods for fitting a line to represent a relationship between data. By the end, you’ll have a better idea...
