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Random forests are effective supervised learning methods applicable to largescale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, specially in the context of problems with very highdimensional input spaces. We propose to study their compressibility by applying a L1based regularization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model accuracy while significantly reducing its space complexity is indeed possible.
Link to the paper http://orbi.ulg.ac.be/handle/2268/124834
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