  The exploration of nuclear mass or binding energy, a fundamental property of
atomic nuclei, remains at the forefront of nuclear physics research due to
limitations in experimental studies and uncertainties in model calculations,
particularly when moving away from the stability line. In this work, we employ
two machine learning (ML) models, Support Vector Regression (SVR) and Gaussian
Process Regression (GPR), to assess their performance in predicting nuclear
mass excesses using available experimental data and a physics-based feature
space. We also examine the extrapolation capabilities of these models using
newly measured nuclei from AME2020 and by extending our calculations beyond the
training and test set regions. Our results indicate that both SVR and GPR
models perform quite well within the training and test regions when informed
with a physics-based feature space. Furthermore, these ML models demonstrate
the ability to make reasonable predictions away from the available experimental
data, offering results comparable to the model calculations. Through further
refinement, these models can be used as reliable and efficient ML tools for
studying nuclear properties in the future.
