A decision tree model is a type of supervised machine learning algorithm used for both classification and regression tasks. It represents decisions and their potential consequences in a tree-like structure, which visualizes how to reach a decision based on certain conditions or features. ### Structure of a Decision Tree: - **Nodes:** Each internal node represents a feature (or attribute) used to make decisions. - **Branches:** The branches represent the outcomes of tests on features, leading to subsequent nodes or leaf nodes.
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The decision tree model computes a function by adaptively querying coordinates of an input. Each internal node specifies a query, its outgoing branches represent possible answers, and a leaf supplies the output. Later queries may depend on earlier answers. The number of queries on the longest root-to-leaf path is the decision-tree depth.[ref]
For example, consider the Boolean function that returns one if any of its input bits is one. Querying in order can stop at the first one, but the all-zero input requires all queries: an unread bit might still change the output. Thus the worst-case query complexity is .