Method, systems, and computer-readable storage devices for updating a prediction model are described. In one aspect, a statistical analysis group assignment may be received. The statistical analysis group assignment may group partition-level worker node and a first set of partition-level worker nodes as a statistical analysis group. A statistical analysis phase may then be executed where a group-level decision tree is generated from statistical data and other statistical data received from the first set of partition-level worker nodes. A decision tree analysis phase may then be executed, where a step decision tree may be generated based on a selection from the group-level tree and other group-level trees received from other statistical analysis groups. The prediction model may be caused to be updated using the step decision tree.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: assigning a first set of partition-level worker nodes to a first statistical analysis group and a second set of partition-level worker nodes to a second statistical analysis group; executing a statistical analysis phase, the statistical analysis phase includes obtaining a first group-level decision tree from first statistical analysis data received from the first statistical analysis group and a second group-level decision tree from second statistical analysis data received from the second statistical analysis group; executing a decision tree analysis phase, the decision tree analysis phase includes using the first group-level decision tree and the second group-level decision tree to generate a step decision tree; and updating a prediction model based on the step decision tree. 2. The method of claim 1, wherein the first set of partition-level worker nodes are each assigned a data partition from a training data set. 3. The method of claim 1, further comprising: receiving a user supplied grouping parameter, wherein the assignment of the first set of partition-level worker nodes to the statistical analysis group and the second set of partition-level worker nodes to the second statistical analysis group operates according to the user supplied grouping parameter. 4. The method of claim 1, wherein the statistical analysis includes generating, by one of the statistical analysis worker nodes of the first set of statistical analysis worker nodes, a histogram of a feature found in samples stored in a corresponding data partition. 5. The method of claim 4, further comprising: communicating the histogram with other statistical analysis worker nodes of the first set of statistical analysis worker nodes; and receiving other histograms from the other statistical analysis worker nodes of the first set of statistical analysis worker nodes. 6. The method of claim 1, wherein generating the step decision tree comprises selecting between the first group-level decision tree and the second group-level decision tree based on respective error levels associated with the first group-level decision tree and the second group-level decision tree. 7. The method of claim 1, wherein generating the step decision tree comprises averaging the first group-level decision tree and the second group-level decision. 8. The method of claim 1, wherein assigning the first set of partition-level worker nodes to the statistical analysis group and the second set of partition-level worker nodes to the second statistical analysis group comprises randomly selecting a first set of data partitions for the first data partition group and a second set of data partitions for the second data partition group. 9. The method of claim 1, further comprising, after updating the prediction model: assigning a third set of partition-level worker nodes to the first statistical analysis group and a fourth set of partition-level worker nodes to the second statistical analysis group, wherein the first set of partition-level worker nodes and the third set of partition-level worker nodes map to different data partitions, the second set of partition-level worker nodes and the fourth set of partition-level worker nodes map to different data partitions; executing another iteration of the statistical analysis phase, the another iteration of the statistical analysis phase includes obtaining a third group-level decision tree from third statistical analysis data received from the third statistical analysis group and a fourth group-level decision tree from fourth statistical analysis data received from the fourth statistical analysis group; executing another iteration of the decision tree analysis phase, the another iteration of the decision tree analysis phase includes using the third group-level decision tree and the fourth group-level decision tree to generate an another step decision tree; and updating the prediction model based on the another step decision tree. 10. A device comprising: a processor, and a machine-readable storage device comprising instructions that, when executed, cause the processor to: receive a statistical analysis group assignment that groups a set of data partitions to a statistical analysis group; receive statistical data from data partitions worker nodes executing local to the set of data partitions; generate a group-level decision tree from the statistical data received from the data partition worker nodes; select the group-level decision tree from other group-level decision trees generated from other statistical analysis groups; and cause a prediction model to be updated based on the group-level decision. 11. The device of claim 10, wherein the instructions further cause, when executed, the processor to: select the group-level decision tree based on losses associated with the group-level decision tree and the other group-level decision trees. 12. A machine-readable storage device comprising instructions that, when executed, cause a processor to: receive, by a partition-level worker node for a data partition, a statistical analysis group assignment that groups the partition-level worker node and a first set of partition-level worker nodes as a statistical analysis group; execute a statistical analysis phase on the partition-level worker node, wherein, in the statistical analysis phase, the instructions cause the processor to generate a group-level decision tree from statistical data derived from the data partition and the other statistical data received from the first set of partition-level worker nodes; execute a decision tree analysis phase on the partition-level worker node, wherein, in the statistical analysis phase, the instructions cause the processor to generate a step decision tree based on a selection made from the group-level tree and other group-level trees received from other statistical analysis groups; and cause a prediction model to be updated using the step decision tree. 13. The machine-readable storage device of claim 12, wherein the statistical data is a histogram of features in the data partition. 14. The machine-readable storage device of claim 12, wherein the statistical analysis assignment, the statistical analysis phase, and the decision tree analysis phase are part of a boosting step, and the instructions, when executed, further cause the processor to receive another statistical analysis assignment in a next boosting step, the another statistical analysis assignment groups the partition-level worker node with a second set of partition-level worker nodes in a different statistical analysis group. 15. The machine-readable storage device of claim 12, wherein the selection made from the group-level tree and the other group-level trees is based on respective error levels associated with the group-level decision tree and the other group-level decision trees.