  Given multiband photometric data from the SDSS DR6, we estimate galaxy
redshifts. We employ a Random Forest trained on color features and
spectroscopic redshifts from 80,000 randomly chosen primary galaxies yielding a
mapping from color to redshift such that the difference between the estimate
and the spectroscopic redshift is small. Our methodology results in tight RMS
scatter in the estimates limited by photometric errors. Additionally, this
approach yields an error distribution that is nearly Gaussian with parameter
estimates giving reliable confidence intervals unique to each galaxy
photometric redshift.
