Comparison between some machine learning algorithms on predicting the spectra of quark–anti-quark bound states

Abstract

This study is devoted to investigate the implementation of machine learning methodologies in the prediction of Quark–anti-Quark bound state spectrum. Predictions are produced by using variety of machine learning (ML) approaches, such as ridge regression, random forest regression, linear regression and K-nearest neighbors regression methods. The forecasts are then evaluated and contrasted in order to determine the optimal performance. Furthermore, systematic comparison of the considered ML methods in terms of percentage of performance is done. Each of the four strategies yielded comparable results. With accuracy of 99%, the ridge regression model exhibited the highest level of predictive performance.