What type of model can classify data into discrete categories?

A classification model is specifically designed to categorize data into distinct classes or labels. Unlike regression models, which predict continuous outcomes, classification models are used when the outcome variable is categorical. This means that the objective is to determine which category a given data point belongs to, based on the data provided during training. Common examples of classification tasks include spam detection in emails (spam or not spam), image recognition (identifying objects within images), and medical diagnosis (disease presence or absence).

Other models mentioned, such as regression, clustering, and descriptive models, serve different purposes. Regression models deal with predicting numeric values rather than categorizing items into discrete classes. Clustering models focus on grouping similar data points together without pre-defined categories, and descriptive models summarize and explore the data's underlying characteristics without making predictions. Therefore, the classification model is the most appropriate choice for tasks that require assigning data into specified discrete categories.