Decision Trees are non-parametric supervised learning algorithms that model decisions as a tree-like structure, where each internal node tests an attribute, each branch represents an outcome, and each leaf node assigns a class label or numerical value. They are highly interpretable and used for both classification and regression tasks.
Decision Trees are a type of machine learning model that makes predictions by following a series of if-then-else rules, much like a flowchart. They are valued for being easy to understand and can be used for tasks like classifying emails or predicting outcomes based on various data points.
ID3, C4.5, C5.0, CART, CHAID, Random Forest, Gradient Boosting
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