Ensemble Learning Methods and AI-Driven Predictive Models: Fusion of Models for Enhanced Change Management and Performance Optimization

Abstract

Ensemble learning methods have become critical in improving the predictive performance of machine learning models, particularly in AI-driven predictive frameworks for Change Management within dynamic project environments. By fusing multiple model outputs, ensemble methods achieve higher accuracy and robustness, helping reduce overfitting and driving better decision-making during change adaptation. This paper provides a comprehensive review of ensemble learning methods, focusing on their principles, types, and applications. We discuss various techniques, including bagging, boosting, and stacking, and analyze their role in optimizing project management through enhanced predictive performance. Furthermore, we explore the importance of model diversity and its impact on ensemble effectiveness. A case study illustrates how these methods can be applied to real-world change management scenarios, improving project outcomes through precise predictive insights.