  The properties of exoplanet host stars are traditionally characterized
through a detailed forward-modeling analysis of high-resolution spectra.
However, many exoplanet radial velocity surveys employ iodine-cell-calibrated
spectrographs, such that the vast majority of spectra obtained include an
imprinted forest of iodine absorption lines. For surveys that use iodine cells,
iodine-free "template" spectra must be separately obtained for precise stellar
characterization. These template spectra often require extensive additional
observing time to obtain, and they are not always feasible to obtain for faint
stars. In this paper, we demonstrate that machine learning methods can be
applied to infer stellar parameters and chemical abundances from
iodine-imprinted spectra with high accuracy and precision. The methods
presented in this work are broadly applicable to any iodine-cell-calibrated
spectrograph. We make publicly available our spectroscopic pipeline, the Cannon
HIRES Iodine Pipeline (CHIP), which derives stellar parameters and 15 chemical
abundances from iodine-imprinted spectra of FGK stars and which has been set up
for ease of use with Keck/HIRES spectra. Our proof-of-concept offers an
efficient new avenue to rapidly estimate a large number of stellar parameters
even in the absence of an iodine-free template spectrum.
