Introduction to Machine Learning with R

Chapter 1. What Is a Model?

There was a time in my undergraduate physics studies that I was excited to learn what a model was. I remember the scene pretty well. We were in a Stars and Galaxies class, getting ready to learn about atmospheric models that could be applied not only to the Earth, but to other planets in the solar system as well. I knew enough about climate models to know they were complicated, so I braced myself for an onslaught of math that would take me weeks to parse. When we finally got to the meat of the subject, I was kind of let down: I had already dealt with data models in the past and hadn’t even realized!

Because models are a fundamental aspect of machine learning, perhaps it’s not surprising that this story mirrors how I learned to understand the field of machine learning. During my graduate studies, I was on the fence about going into the financial industry. I had heard that machine learning was being used extensively in that world, and, as a lowly physics major, I felt like I would need to be more of a computational engineer to compete. I came to a similar realization that not only was machine learning not as scary of a subject as I originally thought, but I had indeed been using it before. Since before high school, even!

Models are helpful because unlike dashboards, which offer a static picture of what the data shows currently (or at a particular slice in time), models can go further and help you understand the future. For example, someone who is working ...