Anca-Elena IORDAN, Florin COVACIU


The purpose of the study presented in this article is to improve the efficiency of estimating the effort required to develop a software product by means of k-Nearest Neighbours machine learning method (KNN). The data set used for training KNN method is NASA93. The evaluation is related to the parameter tuning concept, KNN method being characterized by 2 parameters: the distance used to determine which are neighbours with common characteristics and the number of used neighbours for determining the prediction. To determine which version of KNN method provides the most accurate values for effort, three metrics were calculated: mean absolute error, mean squared error, and median absolute error. Implementation was done using Python programming language and Scikit-learn tool.

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