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.