  We seek decision rules for prediction-time cost reduction, where complete
data is available for training, but during prediction-time, each feature can
only be acquired for an additional cost. We propose a novel random forest
algorithm to minimize prediction error for a user-specified {\it average}
feature acquisition budget. While random forests yield strong generalization
performance, they do not explicitly account for feature costs and furthermore
require low correlation among trees, which amplifies costs. Our random forest
grows trees with low acquisition cost and high strength based on greedy minimax
cost-weighted-impurity splits. Theoretically, we establish near-optimal
acquisition cost guarantees for our algorithm. Empirically, on a number of
benchmark datasets we demonstrate superior accuracy-cost curves against
state-of-the-art prediction-time algorithms.
