Ensemble Methods

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Focusing on ensemble methods in machine learning, this introduction offers a comprehensive overview suitable for beginners. It covers essential algorithms like Boosting, Bagging, and Random Forest, alongside key concepts such as averaging, voting schemes, and diversity measures. Advanced topics include ensemble pruning and clustering, as well as recent developments in semi-supervised, active, cost-sensitive, and class-imbalance learning. The book aims to equip readers with both foundational knowledge and insights into cutting-edge techniques in the field.