Evaluating the Performance of Machine Learning Classifier Algorithms for Software Estimation in Software Development Projects

Abstract

The major aim of this research is to rank the best performing features in order to classify the Software estimation dataset using SVM, Naïve Bayes, Random forest, Decision tree, and KNN classifiers and evaluate their accuracy. Two steps are involved in the classification process: first, the dataset with all attributes is analyzed; second, the information gain methodology is used to rank the attributes, and only the highly rated ones are used to generate the model of classification. Using several folds of cross-validation, we assess the accuracy rank of SVM, Naïve Bayes, Decision tree, Random forest, and KNN classifier