What is a common usage of a Decision Tree in data analysis?

A Decision Tree is commonly used in data analysis to classify categorical outcomes based on feature inputs. This method breaks down a dataset into smaller subsets while an associated decision tree is incrementally developed. It uses features from the input data to make decisions that lead to a specific class label in the outcome. Each branch of the tree corresponds to a feature test, allowing for clear rules to be established that classify observations into predefined categories.

One of the key advantages of using a Decision Tree is its interpretability. It visually represents the decision-making process, allowing stakeholders to easily follow how different features influence the classification of outcomes.

The other options reflect misunderstandings of the Decision Tree's capabilities. While a Decision Tree can indeed handle continuous outcomes as well as categorical ones, its classification task is particularly prominent. Although it can be visualized, this is not its primary purpose. Furthermore, Decision Trees are not limited to just time-series data, as they can be applied to a wide range of modeling contexts beyond sequential datasets.