A decision tree is a machine learning model that uses a tree-like structure to make decisions about data classification. In a decision tree, each internal node represents a question about a specific feature of the data, and the branches represent the possible answers to that question.

The tree is constructed by iteratively selecting the most informative feature to split the data and create additional branches. The process of building the tree continues until a stop condition is reached, such as the maximum depth of the tree or a minimum number of examples in each leaf.

Once the tree is built, it can be used to predict the classification of new examples. New examples are passed through the tree, starting at the root, and branches corresponding to the answers to the questions are followed until a leaf is reached, which provides the final classification.

Decision trees are useful for a variety of classification and regression tasks, and are especially useful for problems where the data characteristics are discrete or categorical. In addition, decision trees can be easily interpreted by humans, which makes them useful for data analysis and understanding. However, decision trees can also be prone to overfitting and may require pruning or assembly techniques to improve performance.