Explain Decision Tree algorithm with the help of a suitable example.

A Decision Tree is a supervised learning algorithm that is widely used for classification and regression tasks. It works by recursively splitting the data into subsets based on feature values, creating a tree-like structure. The tree consists of nodes, branches, and leaves, where each node represents a decision point based on a feature, each branch represents an outcome of that decision, and the leaves ________ ______ __________ ____ ________ ________ _____ ______ ________ _______ ____.
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