One-hot Encoding

One-hot encoding is a technique used in data science and machine learning to represent categorical variables as binary vectors. In this technique, each category is represented by a binary vector with a length equal to the number of categories. The vector has a value of 1 in the position corresponding to the category and 0 in all other positions. One-hot encoding is used to convert categorical data into a format that can be easily processed by machine learning algorithms. It is commonly used in natural language processing, computer vision, and other areas of machine learning where categorical data is prevalent.
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