1. A computerized decision tree training system, comprising: a distributed control processing unit configured to receive input of training data for training a decision tree, the training data including multiple data units, each data unit further including at least one example datum having an associated class label; a plurality of data batch processing units, each data batch processing unit being configured to receive a respective data batch representing a subset of the data units in the training data from the distributed control processing unit, and to evaluate each of a plurality of split functions of the decision tree for the respective data batch, to thereby compute a partial histogram for each split function and each datum in the data batch; and a plurality of node batch processing units, for each of a subset of frontier tree nodes of the decision tree in a respective tree node batch, each node batch processing unit being configured to receive and aggregate the associated partial histograms for each split function to form an aggregated histogram for each split function at each of the frontier tree nodes of the subset, wherein each of the node batch processing units is configured to determine a selected split function for each frontier tree node in the respective subset by computing the split function that produces highest information gain for the frontier tree node; wherein the distributed control processing unit is further configured to reclassify each of the frontier tree nodes as a split node including a respective one of the selected split functions, to expand the decision tree to include new frontier tree nodes branching from the split nodes, and to output the decision tree for installation on a downstream computing device.