What is a decision tree primarily used for?

A decision tree is primarily used for classification and regression tasks. It is a model that breaks down a dataset into smaller and more interpretable parts based on feature values, which helps in making predictions or decisions.

In classification, decision trees help in assigning labels to instances by following the branches of the tree that correspond to the values of the features. For example, in a binary classification task, the model will classify an instance into one of the two categories based on the features being analyzed.

In regression, decision trees predict a continuous value rather than a category. They segment the data into different intervals, and the prediction for an instance is made by averaging the outcomes of the instances falling into the same segment.

The versatility of decision trees allows them to be easily interpretable as they represent decisions in a tree-like structure, making it simple to understand how predictions are made based on the input features. Thus, they are widely utilized in various applications for both classification and regression purposes.