What are decision trees used for in machine learning?

Decision trees are a widely used model in machine learning that serve the primary function of making decisions based on a series of branching conditions. This method involves creating a tree-like structure, where each node represents a feature or attribute of the data, each branch represents a decision rule based on that feature, and each leaf node represents an outcome or class label.

The model operates by starting at the root and moving through the tree based on the decisions made at each node, effectively splitting the dataset into subsets based on specific criteria until reaching a decision. This approach is intuitive and easily interpretable, making decision trees particularly useful for both classification and regression tasks.

The other provided choices do not accurately capture the main purpose of decision trees: performing statistical analysis is more aligned with different techniques, organizing data sets into clusters refers more closely to clustering algorithms rather than decision-making processes, and visualizing neural network outputs pertains to entirely different methods in machine learning, focusing on the interpretability of results from complex models instead of tree-based decision-making.