R for Data Science

Part IV. Model

Now that you are equipped with powerful programming tools we can finally return to modeling. You’ll use your new tools of data wrangling and programming to fit many models and understand how they work. The focus of this book is on exploration, not confirmation or formal inference. But you’ll learn a few basic tools that help you understand the variation within your models.

The goal of a model is to provide a simple low-dimensional summary of a dataset. Ideally, the model will capture true “signals” (i.e., patterns generated by the phenomenon of interest), and ignore “noise” (i.e., random variation that you’re not interested in). Here we only cover “predictive” models, which, as the name suggests, generate predictions. There is another type of model that we’re not going to discuss: “data discovery” models. These models don’t make predictions, but instead help you discover interesting relationships within your data. (These two categories of models are sometimes called supervised and unsupervised, but I don’t think that terminology is particularly illuminating.)

This book is not going to give you a deep understanding of the mathematical theory that underlies models. It will, however, build your intuition about how statistical models work, and give you a family of useful tools that allow you to use models to better understand your data: ...