Ensemble Learning for Improved Model Performance: Analyzing ensemble learning techniques for combining multiple models to improve overall predictive performance

Abstract

Ensemble learning is a powerful approach in machine learning that aims to improve the performance of predictive models by combining multiple base models. This paper provides a comprehensive analysis of ensemble learning techniques and their applications in various domains. We discuss the fundamental concepts of ensemble learning, including bagging, boosting, and stacking, and explore their effectiveness in improving model performance. We also examine advanced ensemble techniques, such as random forests, gradient boosting, and ensemble pruning, highlighting their strengths and limitations. Additionally, we discuss practical considerations for implementing ensemble learning and provide recommendations for selecting the appropriate ensemble method based on the dataset and problem domain. Through experimental evaluation on several benchmark datasets, we demonstrate the superior performance of ensemble learning over individual models. Overall, this paper serves as a comprehensive guide for researchers and practitioners interested in leveraging ensemble learning for improved model performance.