What is a decision tree primarily used for in machine learning?

A decision tree is primarily used for classification and regression tasks because it is a model that makes decisions based on a series of rules derived from the input features of the dataset. In classification tasks, decision trees are utilized to categorize data points into distinct classes by learning from labeled training data. The tree structure allows for clear visibility of decision paths, which aids in understanding the model's predictions.

In regression tasks, decision trees are employed to predict continuous outcomes. The algorithm partitions the feature space into regions and assigns a value (often the mean of the target variable in that region) to each partition. This makes decision trees versatile since they can handle both types of tasks seamlessly.

Other options such as clustering, data preprocessing, and enhancing data collection methods do not correctly describe the main function of decision trees. Clustering involves grouping data points based on similarities without predetermined labels, which is not the objective of a decision tree. Data preprocessing refers to the techniques used to clean and format data before analysis, while enhancing data collection methods pertains to improving how data is gathered, rather than analyzing it once collected. Thus, decision trees stand out specifically for their role in classification and regression tasks.