Which type of model is designed to categorize data into distinct classes?

A classification model is designed to categorize data into distinct classes by mapping input data to predefined categories. This type of model works by analyzing patterns in the training data and creating a decision boundary that separates the classes. For instance, in a binary classification task, the model learns to distinguish between two classes by identifying the features that best differentiate them. The performance of a classification model is often evaluated using metrics such as accuracy, precision, recall, and F1 score, which reflect how well the model predicts the correct class labels for new, unseen data.

In contrast, regression models focus on predicting continuous outcomes rather than categorizing data into distinct classes. Clustering models group similar data points together based on their features but do not assign predefined labels to these groups. Survival models are used to analyze time-to-event data, often in the context of failure rates or survival analysis, and are not suitable for classification tasks either. Thus, a classification model is the appropriate choice for categorizing data into distinct classes.