  The Curie temperature ($T_C$) of binary alloy compounds consisting of 3$d$
transition-metal and 4$f$ rare-earth elements is analyzed by a machine learning
technique. We first demonstrate that nonlinear regression can accurately
reproduce $T_C$ of the compounds. The prediction accuracy for $T_C$ is
maximized when five to ten descriptors are selected, with the rare-earth
concentration being the most relevant. We then discuss an attempt to utilize a
regression-based model selection technique to learn the relation between the
descriptors and the actuation mechanism of the corresponding physical
phenomenon, i.e., $T_C$ in the present case.
