I conjecture that we are built to perceive linear functions very well. They are very easy to visualize, interpret, and explain. Linear regression is very old and was probably the first statistical model.
In this chapter, we will take a machine learning approach to linear regression.
Note that this chapter, similar to the chapter on dimensionality reduction and PCA, involves selecting the best features using linear models. Even if you decide not to perform regression for predictions with linear models, you can select the most powerful features.
Also note that linear models provide a lot of the intuition behind the use of many machine learning algorithms. For example, RBF-kernel SVMs have smooth boundaries, which when looked at up close, look like a line. Thus, SVMs are often easy to explain if, in the background, you remember your linear model intuition.
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