A Comparative Study of Different Ensemble Learning Techniques Using Wisconsin Breast Cancer Dataset

Abstract

The researches in the world of Machine Learning and Artificial Intelligence are increasing as the modern day progresses. By finding manifold applications in wide range of fields the art of Machine Learning only promises to get better. Predictive models form the core of Machine Learning. Better the accuracy better the model is and so is the solution to a particular problem. Ensemble Learning algorithms are a set of algorithms which are used to enhance the predictive accuracy of a predictive model. In this work, a comparative study of different Ensemble Learning techniques has been presented using the Wisconsin Breast Cancer dataset. The primary objective behind using Ensemble learning here is a classification task. This comparative study should help the researchers to find the suitable Ensemble Learning technique for improving their results.