  Structure-property relationships is a key materials science concept that
enables the design of new materials. In the case of materials for application
in radiation environments, correlating radiation tolerance with fundamental
structural features of a material enables materials discovery. Here, we use a
machine learning model to examine the factors that govern amorphization
resistance in the complex oxide pyrochlore ($A_2B_2$O$_7$). We examine the
fidelity of predictions based on cation radii and electronegativities, the
oxygen positional parameter, and the energetics of disordering and amorphizing
the material. No one factor alone adequately predicts amorphization resistance.
We find that, when multiple families of pyrochlores (with different B cations)
are considered, radii and electronegativities provide the best prediction but
when the machine learning model is restricted to only the $B$=Ti pyrochlores,
the energetics of disordering and amorphization are optimal. This work provides
new insight into the factors that govern the amorphization susceptibility and
highlights the ability of machine learning approaches to generate that insight.
