Development and Evaluation of a Machine Learning Classifier for Black Hole Identification

This paper presents the development and evaluation of a RandomForestClassifier for identifying black holes using a combined dataset from NASA/ESA observations and additional astronomical catalogs. The dataset, comprising approximately 894 samples (447 black holes and 447 synthetic non-black holes), was preprocessed to handle missing values and scaled for machine learning. The model achieved an accuracy of 0.9535 on a test set of 86 samples, with perfect recall for non-black holes and high precision for black holes. The study highlights the challenges of overfitting and proposes future improvements.