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.
Artificial Intelligence (AI) technologies are currently being used in companies to transform business processes, drive innovation and improve the quality of life of their [...]
Read More »In order to identify the customer's needs, it is necessary to know their opinion, as this helps to detect where you should improve, what acceptance you [...]
Read More »The world is experiencing exponential growth in data generation on an ever-increasing scale. According to IDC (International Data Corp.
Read More »Artificial intelligence (AI) can change the way sales channels and customers are managed for manufacturers and distributors of consumer products, and can [...]
Read More »
