Performance Analysis of Various Machine Learning Classifiers on Diverse Datasets

Machine learning is used to analyze data from different perspectives, summarize it into useful information, and use that information to predict the likelihood of future events. Classification is one of the main problems in the field of machine learning. The aim here is to study various classification algorithms in machine learning applied on different kinds of datasets. The algorithms used for this analysis are J48, Naive Bayes, multilayer perceptron, and ZeroR. The performance is analyzed using various metrics such as true positive rate, false positive rate, and error rates such as root mean squared error and mean absolute error. The performance of J48 algorithm is better than other algorithms for large datasets. The proposed algorithm still increases the performance in terms of error rates for large datasets. The contemplated algorithm is eventuated by mutating the splitting paradigm in the tree-based algorithms. The experimental analysis demonstrates that the proposed algorithm has reduced error rate as compared with the traditional J48 algorithm.