Which type of model is designed to predict categories for input data?

A classification model is specifically designed to predict categories or classes for given input data. This type of model operates on labeled input data, where each data point is associated with a specific category, enabling the model to learn from these mappings. During the training process, the model identifies the relationships between the input features and the corresponding categories, allowing it to generalize and accurately predict the category for unseen data.

For instance, in a spam email detection system, a classification model would be trained using labeled examples of email messages, classified as either "spam" or "not spam." Once trained, the model can categorize new email messages based on the patterns it learned during training.

In contrast, regression models focus on predicting continuous numeric outcomes rather than discrete categories. Clustering models are used for grouping similar data points together without predefined labels, while association models discover interesting relationships between variables, often in transaction data. Consequently, none of these alternatives align with the primary function of predicting categories, which is the hallmark of classification models.