What type of model is designed to classify input data into categories?

The chosen answer is correct as it accurately identifies the model specifically intended for classifying input data into distinct categories. A classification model operates by taking input data and assigning it to predefined labels or categories based on its features. This process is fundamental in various applications such as spam detection, image recognition, and medical diagnosis, where the goal is to categorize data based on learned patterns from training datasets.

In contrast, a clustering model is designed to group similar data points together without prior knowledge of distinct categories, making it unsuitable for categorization tasks. A regression model, on the other hand, predicts continuous outcomes rather than classifying input data into discrete categories. Meanwhile, a decision tree model is a type of classification model that uses a tree-like structure to make decisions based on feature values; though capable of classification, it is more specific than the broader term "classification model." Thus, while decision trees are a subset of classification models, they don't represent all the capabilities encompassed by classification models in general.