Pruning randomized trees with L1-norm regularization

Growing amount of high dimensional data requires robust analysis techniques. Tree-based ensemble methods provide such accurate supervised learning models. However, the model complexity can become utterly huge depending on the dimension of the dataset. Here we propose a method to compress such ensemble using random tree induced space and L1-norm regularisation. This leads to a drastic pruning, preserving or improving the model accuracy. Moreover, our approach increases robustness with respect to the selection of complexity parameters.