  Precise spectroscopic classification of planet hosts is an important tool of
exoplanet research at both the population and individual system level. In the
era of large-scale surveys, data-driven methods offer an efficient approach to
spectroscopic classification that leverages the fact that a subset of stars in
any given survey has stellar properties that are known with high fidelity.
Here, we use The Cannon, a data-driven framework for modeling stellar spectra,
to train a generative model of spectra from the Gaia Data Release 3 Radial
Velocity Spectrometer. Our model derives stellar labels with precisions of 72 K
in Teff , 0.09 dex in log g, 0.06 dex in [Fe/H], 0.05 dex in [{\alpha}/Fe] and
1.9 km/s in vbroad for main-sequence stars observed by Gaia DR3 by transferring
GALAH labels, and is publicly available at
https://github.com/isabelangelo/gaiaspec. We validate our model performance on
planet hosts with available Gaia RVS spectra at SNR>50 by showing that our
model is able to recover stellar parameters at {\geq}20% improved accuracy over
the existing Gaia stellar parameter catalogs, measured by the agreement with
high-fidelity labels from the Spectroscopic Observations of Cool Stars (SPOCS)
survey. We also provide metrics to test for stellar activity, binarity, and
reliability of our model outputs and provide instructions for interpreting
these metrics. Finally, we publish updated stellar labels and metrics that flag
suspected binaries and active stars for Kepler Input Catalog objects with
published Gaia RVS spectra.
