The idea that linear models are linear in the parameters and not the data is a bit confusing. I know the effect of this is that you fit curves with "linear" models, but I don't feel like I fully understand this. Can you explain further or link to some good resources?
Each data point is a bunch of features x_1, x_2, ..., x_n.
You can make new features for your data points using whatever functions you like -- it doesn't matter if they're linear. Let's say we add two new features x_{n+1} = f(x_1, x_2) and x_{n+2} = g(x_2, x_3).
Now if we train a linear model on the new expanded set of features, it's linear in those features. It's not linear in the original data though, because of the new features that we introduced: x_{n+1} and x_{n+2}.
