  $Q_\beta$ represents one of the most important factors characterizing
unstable nuclei, as it can lead to a better understanding of nuclei behavior
and the origin of heavy atoms. Recently, machine learning methods have been
shown to be a powerful tool to increase accuracy in the prediction of diverse
atomic properties such as energies, atomic charges, volumes, among others.
Nonetheless, these methods are often used as a black box not allowing
unraveling insights into the phenomena under analysis. Here, the
state-of-the-art precision of the $\beta$-decay energy on experimental data is
outperformed by means of an ensemble of machine-learning models. The
explainability tools implemented to eliminate the black box concern allowed to
identify uncertainty and atomic number as the most relevant characteristics to
predict $Q_\beta$ energies. Furthermore, physics-informed feature addition
improved models' robustness and raised vital characteristics of theoretical
models of the nuclear structure.
