Ensemble Techniques to improve the performance of the High Dimensional MultiClass Algorithms

Abstract

Ensemble plays a major role in machine learning algorithms, and it can improve the performance of the single model by combining two or more models. It can be able to combine a number of different models and comes out with a promising result. There are several ensemble techniques such as bagging, boosting, and stacking each of which performs in its own way and produces the results. In this work the different techniques of ensembling are being explored and has been tested its working on the sample dataset. The results are varying in performance and suits well for the taken data points. Keywords: ensemble, stacking, boosting, bagging, ensemble learners