EXPLORING THE POTENTIAL OF ENSEMBLE LEARNING TECHNIQUES TO ENHANCE ACCURACY AND ROBUSTNESS IN COMPLEX REAL-WORLD MACHINE LEARNING APPLICATIONS

Abstract

This research paper investigates the extent to which ensemble learning techniques can improve the accuracy and robustness of machine learning models in complex real-world applications. The paper examines various ensemble methods, including boosting, random forests, bagging, and SVMs, and evaluates their performance on a range of metrics. The findings highlight the effectiveness of ensemble learning in enhancing model performance, with calibrated boosted trees emerging as the top-performing algorithm across multiple metrics.