L1-based compression of random forest models

@inproceedings{Joly2012L1basedCO,
  title={L1-based compression of random forest models},
  author={Arnaud Joly and François Schnitzler and Pierre Geurts and Louis Wehenkel},
  booktitle={ESANN},
  year={2012}
}
Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, spe- cially in the context of problems with very high-dimensional input spaces. We propose to study their compressibility by applying a L1-based regu- larization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model… CONTINUE READING

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