A performance evaluation of neural network features and functions settings on the model accuracy

Abstract

Not only in sports is a neural network the most used type of artificial intelligence. With software development, anyone can create a neural network model, but little is known about how to prepare the data and how to set up the model algorithms to their maximum performance. For these reasons, this study aims to determine whether features or function settings have a greater effect on model accuracy. An initial feature dataset (n = 18882) was obtained from publicly available sources. Each of the six different feature settings consisted of 96 models. A total of 384 models were created, in which their testing accuracy and the percentage difference between the training and testing phases were further analyzed. No statistically significant differences were found between the accuracy of the functions settings, but statistically significant differences were confirmed between the feature settings. The study found that feature settings, especially the reduction of the number of outputs, are a more important factor in increasing the model accuracy, than function settings. Although the literature focuses more on the function setting and sets feature setting is taken rather as a type of how to improve the model.