Paper Title
Ensemble Methods to Produce Improved ML Results

Abstract
This Ensemble approaches can increase machine learning models' overall performance, stability, and generalization. They can successfully reduce over fitting and handle complex data interactions by integrating varied models. Ensemble approaches, on the other hand, are computationally more expensive and may necessitate careful tuning to produce ideal results. This paper presents a comprehensive review of ensemble methods, covering advancements, applications, and a comparative analysis of various techniques. The paper gives performance analysis and trade-offs of ensemble methods to permit objective comparison. It helps to determine the impact of ensemble size, model diversity, and computational complexity on performance, as well as assessment criteria such as accuracy, precision, recall, and F1score. The comparison study also identifies conditions in which various ensemble approaches shine and provides guidance in selecting the best strategy for a given challenge. Keywords - Ensemble Methods, Bagging, Boosting, Random Forest, Stacking.