Beyond One-Hot: an exploration of categorical variables

In machine learning, data are king. The algorithms and models used to make predictions with the data are important, and very interesting, but ML is still subject to the idea of garbage-in-garbage-out. With that in mind, let's look at a little subset of those input data: categorical variables. Categorical variables (wiki) are those that represent a fixed number of possible values, rather than a continuous number. Each value assigns the measurement to one of those finite groups, or categories. They differ from ordinal variables in that the distance from one category to another ought to be equal regardless of the number of categories, as opposed to ordinal variables which have some intrinsic ordering.