One-Hot Encoding

machine-learning

Definition

One-Hot Encoding

One-hot encoding is an encoding technique that converts categorical data into a numerical format that can be provided to machine learning algorithms to improve their predictions.

Example: The colour of a cup can be red, green or blue. One-hot encoding would encode a red-coloured cup as:

$$\left[\begin{array}{c}\text{red}\\ \text{green}\\ \text{blue}\end{array}\right]=\left[\begin{array}{c}1\\ 0\\ 0\end{array}\right]$$

where $1$ represents that the “red flag” is marked active and the others are marked inactive, $0$.