We collect a sample of stars observed both in LAMOST and Gaia which have colors implying a temperature hotter than 7000 K. We train a machine learning algorithm on LAMOST spectroscopic data which has been tagged with stellar classifications and metallicities, and use this machine to construct a catalog of Blue Horizontal Branch stars (BHBs) with metallicity information. Another machine is trained using Gaia parallaxes to predict absolute magnitudes for these stars. The final catalog of 13,693 BHBs is thought to be about 86% pure, with $sigma_{[Fe/H]}sim$0.35 dex and $sigma_{G}sim$0.31 mag. These values are confirmed via comparison to globular clusters, although a covariance error seems to affect our magnitude and abundance estimates. We analyze a subset of this catalog in the Galactic Halo. We find that BHB populations in the outer halo appear redder, which could imply a younger population, and that the metallicity gradient is relatively flat around [Fe/H] = -1.9 dex over our sample footprint. We find that our metal rich BHB stars are on more radial velocity dispersion dominated orbits ($beta sim 0.70$) at all radii than our metal poor BHB stars ($beta sim 0.62$).
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