Ensemble Machine Learning AdaBoost with NBTree Model for Placement Data Analysis

This paper proposed a novel Ensemble Machine Learning (EML) method for placement data classification analysis. AdaBoost Method with various tree based classifiers are used as the base classifier for classification in placement data set. Here Adaboost is used to improving the nominal class classifier and increase the performance in time complexity and classification accuracy in the student data set with placement status as the nominal class attribute. In this paper, AdaBoost classifier with base classifiers decision stump, NBTree and Random Forest are used to classify the student data and analyse the performance of these classifiers with and without AdaBoost. The results shows that the NBTree shows best accuracy results than decision stump and Random Forest.