Decision Tree
Decision Tree is a supervised machine learning algorithm used for classification and regression analysis. It is a tree-like model where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents the outcome. The algorithm works by recursively splitting the data into subsets based on the most significant feature until a stopping criterion is met. The stopping criterion can be a maximum depth of the tree, a minimum number of samples required to split an internal node, or a minimum number of samples required to be at a leaf node. Decision Trees are easy to interpret and visualize, and can handle both categorical and numerical data. However, they are prone to overfitting and can be unstable if the data is noisy or the tree is too deep.
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