  Interplanetary magnetic flux ropes (MFRs) are commonly observed structures in
the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs
(SMFRs) depending on whether they are associated with coronal mass ejections.
We apply machine learning to systematically compare SMFRs, MCs, and ambient
solar wind plasma properties. We construct a dataset of 3-minute averaged
sequential data points of the solar wind's instantaneous bulk fluid plasma
properties using about twenty years of measurements from \emph{Wind}. We label
samples by the presence and type of MFRs containing them using a catalog based
on Grad-Shafranov (GS) automated detection for SMFRs and NASA's catalog for MCs
(with samples in neither labeled non-MFRs). We apply the random forest machine
learning algorithm to find which categories can be more easily distinguished
and by what features. MCs were distinguished from non-MFRs with an AUC of 94%
and SMFRs with an AUC of 89% and had distinctive plasma properties. In
contrast, while SMFRs were distinguished from non-MFRs with an AUC of 86%, this
appears to rely solely on the $\langle B \rangle$ > 5 nT threshold applied by
the GS catalog. The results indicate that SMFRs have virtually the same plasma
properties as the ambient solar wind, unlike the distinct plasma regimes of
MCs. We interpret our findings as additional evidence that most SMFRs at 1 au
are generated within the solar wind, and furthermore, suggesting that they
should be considered a salient feature of the solar wind's magnetic structure
rather than transient events.
