One Hot Encoding

One-hot encoding is a process used in machine learning to convert categorical variables into a binary vector representation. Each category is represented by a vector with a single high (1) value and the rest low (0), ensuring that each category is mutually exclusive in the feature space.

Let’s consider an example with a simple dataset of colors: [“Red”, “Green”, “Blue”].

In one-hot encoding, each color will be represented by a vector where only one element is ‘1’ and the rest are ‘0’. The length of each vector is equal to the number of unique categories (in this case, 3). Here’s how it would look:

• “Red” would be encoded as [1, 0, 0]
• “Green” as [0, 1, 0]
• “Blue” as [0, 0, 1]

In this representation, each vector uniquely identifies one color, with no overlap between the categories.