Ensemble Learning Approach to Improve Existing Models


Authors : Agrim Mehra, Priyansha Tripathy, Ashhad Faridi, Ayes Chinmay

Volume/Issue : Volume 4 - 2019, Issue 12 - December

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://bit.ly/34q2Lju

Abstract : Machine learning is becoming an exciting field because it can be applied to the different problems we face in our daily lives. An essential function of machine learning is predicting the possibility of any future event. Improving this accuracy of prediction has always been one of the most challenging aspects of Machine Learning. In this paper we have tried to improve the accuracy by Combining the Ensemble Learning [1] approach of Bagging [2] and Boosting [3] with Linear regression. The problem of extrapolation in case of Bagging techniques is also explored and corrected using Ensemble methods. The combination will yield better results when used in the case of Bagging techniques as compared to the results given by individual models. Simulations of these algorithms are achieved in R and are further demonstrated in future sections.

Keywords : Ensemble Learning, Bagging, Boosting, Multiple Linear Regression, RMSE, Random Forest, Extrapolation.

Machine learning is becoming an exciting field because it can be applied to the different problems we face in our daily lives. An essential function of machine learning is predicting the possibility of any future event. Improving this accuracy of prediction has always been one of the most challenging aspects of Machine Learning. In this paper we have tried to improve the accuracy by Combining the Ensemble Learning [1] approach of Bagging [2] and Boosting [3] with Linear regression. The problem of extrapolation in case of Bagging techniques is also explored and corrected using Ensemble methods. The combination will yield better results when used in the case of Bagging techniques as compared to the results given by individual models. Simulations of these algorithms are achieved in R and are further demonstrated in future sections.

Keywords : Ensemble Learning, Bagging, Boosting, Multiple Linear Regression, RMSE, Random Forest, Extrapolation.

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