  When dealing with subjective, noisy, or otherwise nebulous features, the
"wisdom of crowds" suggests that one may benefit from multiple judgments of the
same feature on the same object. We give theoretically-motivated `feature
multi-selection' algorithms that choose, among a large set of candidate
features, not only which features to judge but how many times to judge each
one. We demonstrate the effectiveness of this approach for linear regression on
a crowdsourced learning task of predicting people's height and weight from
photos, using features such as 'gender' and 'estimated weight' as well as
culturally fraught ones such as 'attractive'.
