P. Geurts & G. Louppe; JMLR W&CP
Learning to rank with extremely randomized trees
In this paper, we report on our experiments on the Yahoo! Labs Learning to Rank
challenge organized in the context of the 23rd International Conference of Machine
Learning (ICML 2010). We competed in both the learning to rank and the transfer
learning tracks of the challenge with several tree-based ensemble methods, including
Tree Bagging (?
), Random Forests (?
), and Extremely Randomized Trees (?
methods ranked 10th in the ﬁrst track and 4th in the second track. Although not at the
very top of the ranking, our results show that ensembles of randomized trees are quite
competitive for the “learning to rank” problem. The paper also analyzes computing times of
our algorithms and presents some post-challenge experiments with transfer learning
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