Question: Explain the term 'one-hot encoding' and its application in machine learning.

Answer: One-hot encoding is a technique used to represent categorical variables as binary vectors. Each category is represented by a unique binary digit, and this encoding is valuable when working with algorithms that require numerical input.

Example:

Converting categorical variables like 'color' into binary vectors (e.g., red: [1, 0, 0], blue: [0, 1, 0], green: [0, 0, 1]).