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.
5 Big Data challenges can be highlighted which are defined as V (volume, velocity, veracity, variety and value). R. Narasimhan discussed 3V with [...]
Read More »Since 2008, several countries have enacted legislation that recognizes the importance of integrating artificial intelligence (AI) into key areas of life [...]
Read More »After the revolutions led by coal, electricity, and then electronics, society is now witnessing a fourth revolution in the energy sector.
Read More »Cloud computing services or solutions, whether in Spain or anywhere else in the world, are infrastructures, platforms or systems that are used in the cloud.
Read More »
