MACHINE LEARNING CLASSIFICATION OF STARS, GALAXIES, AND QUASARS

The objective of this study was to create a predictive model to classify stars, galaxies, and quasars, along with comparing different classification models to find the superior one. I hypothesized that it was possible to successfully train a machine learning model to classify stars, galaxies, and quasars using astronomical data provided by the Sloan Digital Sky Survey (SDSS). A multinomial logistic regression model has been trained and tested. It had an accuracy of 0.87, a weighted average precision, recall, and an f-1 score of 0.87, and a cross-validation accuracy score of 0.8664. The next model, a decision tree, had an accuracy of 0.99, weighted average precision, recall, and an f-1 score of 0.99, a cross-validation accuracy score of 0.99, and a cross-validation accuracy score of 0.9858. The decision tree model had significantly superior performance compared to the logistic regression model and was a good fit and accurate classifier for stars, galaxies, and quasars, proving my hypothesis to be correct. The model from this study could be used as a reliable classification tool for a wide variety of astronomical purposes to accelerate the expansion of the sample sizes of stars, galaxies, and especially quasars.