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Classification is one of the most important tasks in data mining, where a model is trained using historical data to predict the class label of new instances. Decision Tree, Naïve Bayes, k-Nearest Neighbour (k-NN), and Support Vector Machine (SVM) are among the most widely used classification algorithms. Each algorithm differs in its learning approach, computational complexity, interpretability, and suitability for different types of datasets. A Decision Tree is a supervised learning algorithm that classifies data by recursively splitting it into subsets based on the most informative attributes. Internal nodes represent decision conditions, branches represent outcomes of those conditions, and leaf nodes represent class labels. Algorithms such as ID3, C4.5, and CART are commonly _____ ___ _________ ________ _____ _______ _______ __________ _______ ________ __________.
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