Question: What is the purpose of the term 'one-hot encoding' 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 value, with only one bit set to 1 and the rest set to 0. It is commonly used in machine learning algorithms that cannot work directly with categorical data. |
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