Programming Machine Learning

Adding More Dimensions

In the previous chapter, we coded a gradient descent-based version of our learning program. This souped-up program can potentially scale to complex models with more than one variable.

In a moment of weakness, we mentioned that opportunity to our friend Roberto. That was a mistake. Now Roberto is all pumped up about forecasting pizza sales from a bunch of different input variables besides reservations, such as the weather, or the number of tourists in town.

This is going to be more work for us—and yet, we can’t blame Roberto for wanting to add variables to the model. After all, the more variables we consider, the more likely it is that we’ll get accurate predictions of pizza sales.

Let’s start with a souped-up version of ...