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A Decision Tree is a type of supervised machine learning algorithm used for classification and regression tasks. It mimics human decision-making by representing data decisions in a tree-like structure, where each internal node represents a test on a feature, each branch represents an outcome of the test, and each leaf node represents a final decision or prediction. Decision trees are intuitive, easy to visualize, and useful for explaining model logic. They can be prone to overfitting but are often used as components in more advanced methods like Random Forests and Gradient Boosted Trees.
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