Categorical data consists of values drawn from a finite set of categories rather than from a continuous numeric range. In machine learning, categorical features often require encoding so that models can learn distinctions among classes such as product type, country, or risk level.
Features having a specific set of possible values. For example, consider a categorical feature named , which can only have one of the following three possible values: - - - By representing as a categorical feature, a model can learn the differing impacts of , , and on driver behavior. Categorical features are sometimes called discrete features. Contrast with numerical data. See Working with categorical data in Machine Learning Crash Course for more information.