3 New Ensemble Learning Books Reshaping AI in 2025

When Gautam Kunapuli set out to write this book, he drew on his 15 years bridging academia and industry to tackle the gap between ensemble theory and practical use. You’ll explore diverse ensemble techniques like bagging, boosting, and gradient boosting through hands-on case studies — from medical diagnosis to sentiment analysis — that reveal how multiple models combine for greater accuracy. The book digs into feature engineering, interpretability, and adapting ensembles to different data types, making it ideal if you’re a Python programmer aiming to build more explainable, high-performing machine learning systems. It’s straightforward and code-focused, helping you grasp not just what these methods are but how to apply them effectively.

After analyzing student performance data from Constantine the Philosopher University, Jovial Niyogisubizo developed a stacking ensemble method that combines Random Forest, XGBoost, Gradient Boosting, and Feed-forward Neural Networks to improve dropout prediction accuracy. You’ll learn how integrating diverse machine learning models can pinpoint at-risk university students even with limited datasets, focusing on online activity and partial achievements. This approach helps educational authorities identify influential factors behind student attrition, enabling targeted interventions. If you’re involved in educational data science or student success initiatives, this book offers a concise yet insightful exploration of ensemble techniques tailored to real-world dropout challenges.

This tailored book explores the latest ensemble learning advancements shaping machine learning in 2025. It covers emerging ensemble techniques, contemporary algorithmic developments, and new approaches that have redefined model accuracy and robustness. By focusing on your specific interests and background, this personalized guide delves into recent discoveries and evolving research, revealing how ensemble methods are being refined to tackle complex real-world problems effectively. Designed to match your goals, it addresses cutting-edge ensemble architectures and model combination strategies, providing insights into practical applications across various domains. This focused exploration helps you stay current with rapidly advancing knowledge in ensemble learning while aligning with your unique learning path and ambitions.

Ravi Ray Chaudhari and Himanshi Agrawal explore a specialized niche within machine learning by focusing on the classification of protein sequences using ensemble learning techniques. The book delves into how combining multiple predictive models can improve the accuracy of identifying protein structures and their evolutionary functions, a task vital in bioinformatics given the surge in protein data. You'll find specific discussions on feature extraction methods and classification challenges that matter for anyone working with biological data or interested in advanced applications of ensemble learning. This concise 56-page volume suits practitioners and researchers aiming to bridge machine learning with computational biology rather than general AI enthusiasts.