In this study, we developed a simple linear model to predict the acidity of oxides on the Smith scale using four relevant input features: electronegativity, metal valence (raised to the power of 1/3), ionic radius, and dipole polarizability. The linear model outperformed a more complex 1D convolutional neural network, demonstrating superior accuracy and interpretability.