# One-hot encoding

One-hot encoding refers to a way of transforming data into vectors where all components are 0, except for one component with a value of 1, e,g.: $0 = [1, 0, 0, 0, 0]^T$ $1 = [0, 1, 0, 0, 0]^T$ $\ldots$ $4 = [0, 0, 0, 0, 1]^T$ and so on.

One-hot encoding can make it easier for machine learning algorithms to manipulate and learn categorical variables.