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Machine Learning for Hackers by John Myles White, Drew Conway

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Chapter 6. Regularization: Text Regression

Nonlinear Relationships Between Columns: Beyond Straight Lines

While we told you the truth in Chapter 5 when we said that linear regression assumes that the relationship between two variables is a straight line, it turns out you can also use linear regression to capture relationships that aren’t well-described by a straight line. To show you what we mean, imagine that you have the data shown in panel A of Figure 6-1.

Modeling nonlinear data: (A) visualizing nonlinear relationships; (B) nonlinear relationships and linear regression; (C) structured residuals; (D) results from a generalized additive model

Figure 6-1. Modeling nonlinear data: (A) visualizing nonlinear relationships; (B) nonlinear relationships and linear regression; (C) structured residuals; (D) results from a generalized additive model

It’s obvious from looking at this scatterplot that the relationship between X and Y isn’t well-described by a straight line. Indeed, plotting the regression line shows us exactly what will go wrong if we try to use a line to capture the pattern in this data; panel B of Figure 6-1 shows the result.

We can see that we make systematic errors in our predictions if we use a straight line: at small and large values of x, we overpredict y, and we underpredict y for medium values of x. This is easiest to see in a residuals plot, as shown in panel C of Figure 6-1. In this plot, you can see all of the structure of the original data set, as none of the structure is captured by the default linear regression model.

Using ggplot2’s geom_smooth function ...

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