  Precision photometric redshifts will be essential for extracting cosmological
parameters from the next generation of wide-area imaging surveys. In this paper
we introduce a photometric redshift algorithm, ArborZ, based on the
machine-learning technique of Boosted Decision Trees. We study the algorithm
using galaxies from the Sloan Digital Sky Survey and from mock catalogs
intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show
that it improves upon the performance of existing algorithms. Moreover, the
method naturally leads to the reconstruction of a full probability density
function (PDF) for the photometric redshift of each galaxy, not merely a single
"best estimate" and error, and also provides a photo-z quality figure-of-merit
for each galaxy that can be used to reject outliers. We show that the stacked
PDFs yield a more accurate reconstruction of the redshift distribution N(z). We
discuss limitations of the current algorithm and ideas for future work.
