Boosting Accuracy of Supervised Algorithm with the Introduction of Helper Constant

Abstract

If any machine learning algorithm the success of it is based on the accuracy. If the algorithm accuracy decreases due to the increase in the size of the feature vector then Boosting techniques help the algorithms (in this case it is Regression algorithms) to maintain or improve the accuracy. In this paper we have dealt with the supervised algorithms. The accuracy of the supervised algorithms is improved with the introduction of wrapper constants so that the accuracy is improved with the large dataset as well as with increase in features.