  To accurately interpret the observed properties of exoplanets, it is
necessary to first obtain a detailed understanding of host star properties.
However, physical models that analyze stellar properties on a per-star basis
can become computationally intractable for sufficiently large samples.
Furthermore, these models are limited by the wavelength coverage of available
spectra. We combine previously derived spectral properties from the
Spectroscopic Properties of Cool Stars (SPOCS) catalog (Brewer et al. 2016)
with generative modeling using The Cannon to produce a model capable of
deriving stellar parameters ($\log g$, $T_{\mathrm{eff}}$, and $v\sin i$) and
15 elemental abundances (C, N, O, Na, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Fe, Ni,
and Y) for stellar spectra observed with Keck Observatory's High Resolution
Echelle Spectrometer (HIRES). We demonstrate the high accuracy and precision of
our model, which takes just $\sim$3 seconds to classify each star, through
cross-validation with pre-labeled spectra from the SPOCS sample. Our trained
model, which takes continuum-normalized template spectra as its inputs, is
publicly available at https://github.com/malenarice/keckspec. Finally, we
interpolate our spectra and employ the same modeling scheme to recover labels
for 477 stars using archival stellar spectra obtained prior to Keck's 2004
detector upgrade, demonstrating that our interpolated model can successfully
predict stellar labels for different spectrographs that have (1) sufficiently
similar systematics and (2) a wavelength range that substantially overlaps with
that of the post-2004 HIRES spectra.
