Deep Learning for Coders with fastai and PyTorch

Chapter 9. Tabular Modeling Deep Dive

Tabular modeling takes data in the form of a table (like a spreadsheet or CSV). The objective is to predict the value in one column based on the values in the other columns. In this chapter, we will look at not only deep learning, but also more general machine learning techniques like random forests, as they can give better results depending on your problem.

We will look at how we should preprocess and clean the data as well as how to interpret the result of our models after training, but first we will see how we can feed columns that contain categories into a model that expects numbers by using embeddings.

Categorical Embeddings

In tabular data, some columns may contain numerical data, like “age,” while others contain string values, like “sex.” The numerical data can be directly fed to the model (with some optional preprocessing), but the other columns need to be converted to numbers. Since the values in those correspond to different categories, we often call this type of variables categorical variables. The first type are called continuous variables.

Jargon: Continuous and Categorical Variables

Continuous variables are numerical data, such as “age,” that can be directly fed to the model, since you can add and multiply them directly. Categorical variables contain a number of discrete levels, such as “movie ID,” for which addition and multiplication don’t have meaning (even if they’re stored as numbers).

At the end of 2015, the Rossmann ...