Stop One-Hot Encoding Your Categorical Variables.

One-hot encoding, otherwise known as dummy variables, is a method of converting categorical variables into several binary columns, where a 1 indicates the presence of that row belonging to that category. It is, pretty obviously, not a great a choice for the encoding of categorical variables from a machine learning perspective. Most apparent is the heavy amount of dimensionality it adds, and it is common knowledge that generally a lower amount of dimensions is better. For example, if we were to have a column representing a US state (e.g. California, New York), a one-hot encoding scheme would result in fifty additional dimensions.