1. A computer-implemented method of optimizing a machine-learned ranking algorithm, comprising: a. implementing the algorithm as a sequence of decision trees involving decision nodes based on threshold values of features; b. analyzing the threshold feature values to determine threshold intervals for each feature; c. grouping the decision trees according to the feature used in a root decision node; d. coalescing the decision trees within each group to form a coalesced group tree for each group; and e. coalescing the coalesced group trees to form a coalesced tree that implements the algorithm.