ANALYSIS OF ENSEMBLE METHODS OF MACHINE LEARNING ALGORITHMS

Abstract

This section examines ensemble learning methods and their importance in improving prediction accuracy and stability over single machine learning models. It focuses on the main ensemble approaches—bagging, boosting, and stacking—and explains their working principles and structural differences. The analysis also highlights homogeneous and heterogeneous ensembles, sequential and parallel strategies, and the growing role of ensemble deep learning in intelligent prediction systems.