Decision Trees
Decision Trees are a type of supervised learning algorithm used in data mining and machine learning. They are used for both classification and regression analysis. Decision Trees are constructed by recursively splitting the dataset into smaller subsets based on the most significant features. The goal is to create a model that predicts the value of a target variable based on several input variables. The decision tree algorithm creates a tree-like model of decisions and their possible consequences. Each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a numerical value. Decision Trees are easy to understand and interpret, and they can handle both categorical and numerical data. However, they are prone to overfitting and can be unstable if the data is noisy.
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