Ensemble Learning Methods - Fusion of Models: Analyzing ensemble learning methods for combining multiple models to improve predictive performance and reduce overfitting

Abstract

Ensemble learning methods have emerged as powerful tools for improving the performance of machine learning models by leveraging the diversity of multiple models. By combining the predictions of individual models, ensemble methods can often achieve higher accuracy and robustness compared to any single model. This paper provides a comprehensive review of ensemble learning methods, focusing on their principles, types, and applications. We discuss various techniques for constructing ensemble models, including bagging, boosting, and stacking, along with their strengths and limitations. Additionally, we explore the concept of diversity in ensemble models and its impact on performance. Finally, we present a case study demonstrating the effectiveness of ensemble learning in a real-world predictive modeling task. Through this paper, we aim to provide researchers and practitioners with a thorough understanding of ensemble learning methods and their potential for improving predictive performance in machine learning applications.