L1-based compression of random forest models

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 accuracy while significantly reducing its space complexity is indeed possible.