Explore the powerful world of Ensemble Methods with this updated foundational text. Ideal for machine learning practitioners and researchers, this book delves into combining multiple learners to achieve superior accuracy. Discover key algorithms like Boosting and Bagging, and understand their effectiveness in real-world applications. This second edition, published by Taylor & Francis on August 13, 2025, brings you up-to-date with significant advances since 2012. It addresses theoretical questions, such as AdaBoost’s resistance to overfitting, and explores new applications like anomaly detection with Isolation Forest. Enhance your understanding of ensemble methods and their expansion beyond conventional supervised learning with this comprehensive guide from The Bookish Owl.