  Accurately predicting the z-component of the interplanetary magnetic field,
particularly during the passage of an interplanetary coronal mass ejection
(ICME), is a crucial objective for space weather predictions. Currently, only a
handful of techniques have been proposed and they remain limited in scope and
accuracy. Recently, a robust machine learning (ML) technique was developed for
predicting the minimum value of Bz within ICMEs based on a set of 42
'features', that is, variables calculated from measured quantities upstream of
the ICME and within its sheath region. In this study, we investigate these
so-called explanatory variables in more detail, focusing on those that were (1)
statistically significant; and (2) most important. We find that number density
and magnetic field strength accounted for a large proportion of the
variability. These features capture the degree to which the ICME compresses the
ambient solar wind ahead. Intuitively, this makes sense: Energy made available
to CMEs as they erupt is partitioned into magnetic and kinetic energy. Thus,
more powerful CMEs are launched with larger flux-rope fields (larger Bz), at
greater speeds, resulting in more sheath compression (increased number density
and total field strength).
