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Machine Learning focuses on the construction and study of systems that canlearn from data. This is connected with the classification problem, whichusually is what Machine Learning algorithms are designed to solve. When amachine learning method is used by people with no special expertise in machinelearning, it is important that the method be robust in classification, in thesense that reasonable performance is obtained with minimal tuning of theproblem at hand. Algorithms are evaluated based on how robust they can classifythe given data. In this paper, we propose a quantifiable measure of robustness,and describe a particular learning method that is robust according to thismeasure in the context of classification problem. We proposed Adaptive Boosting(AdaBoostM1) with J48(C4.5 tree) as a base learner with tuning weight threshold(P) and number of iterations (I) for boosting algorithm. To benchmark theperformance, we used the baseline classifier, AdaBoostM1 with Decision Stump asbase learner without tuning parameters. By tuning parameters and using J48 asbase learner, we are able to reduce the overall average error rate ratio(errorC/errorNB) from 2.4 to 0.9 for development sets of data and 2.1 to 1.2for evaluation sets of data.

Enhanced version of AdaBoostM1 with J48 Tree learning method