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Abstract : |
A common criticism of many methods for constructing tree models is that a single tree or nested sequence of trees is produced, and that much uncertainty about the tree structure is ignored. Recent search algorithms (bumping, boosting, simulated annealing, MCMC) address this problem by finding a much richer collection of trees. They lead to an embarrassment of riches, in that it may be difficult to make sense of the resultant forest. Quite often, the problem may not be as bad as it seems: although hundreds of distinct trees are identified, many will differ only at a few nodes. Other trees may have different topologies, but produce similar partitions of the predictor space. By defining several distance metrics on trees, we summarize a forest of trees by several archetypes and associated clusters. 1, |
