Categorical values are those that represent a category or group of data, in contrast to numerical values, which represent quantities. In machine learning, categorical values are important because many algorithms require data to be represented numerically.
For example, categorical values can represent the make of a car, the colour of a product, the category of an image or the type of a question. These values can be represented as character strings or as integers representing a particular category.
When processing categorical values in a machine learning model, it is necessary to encode them into a numerical form that can be interpreted by the algorithm. A common technique for encoding categorical values is one-hot encoding, which converts each categorical value into a binary vector in which only one of the elements is "1" and the others are "0".
It is important to note that the choice of the appropriate encoding of categorical values can significantly affect the performance of the machine learning model.
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