What is a decision tree used for in machine learning?

A decision tree is used in machine learning primarily as a flowchart-like structure for making decisions regarding classification and regression tasks. This model establishes a tree-like graph of decisions where each internal node denotes a feature (or attribute), each branch represents a decision rule, and each leaf node signifies the outcome.

The structure of the decision tree allows for easy interpretation and visualization of the model. It systematically breaks down a dataset into smaller, more manageable subsets while at the same time making various decisions at each node. In the context of classification, the tree categorizes input data into distinct classes, while in regression, it predicts continuous values.

The use cases for decision trees in industry and research are broad, making them a popular choice for both novice and experienced data scientists. They can handle both categorical and numerical data and are also robust to outliers.

Other options, while related to machine learning, do not accurately reflect the specific purpose of decision trees. Clustering involves grouping similar data points, feature selection pertains to identifying relevant features for modeling, and visualizing neural networks does not involve decision trees at all. Hence, the primary function of a decision tree as a flowchart-like structure for decision-making in classification and regression tasks makes it a vital tool within the field of