Random model trees: an effective and scalable regression method

Abstract

We present and investigate ensembles of randomized model trees as a novel regression method. Such ensembles combine the scalability of tree-based methods with predictive performance rivaling the state of the art in numeric prediction. An extensive empirical investigation shows that Random Model Trees produce predictive performance which is competitive with state-of-the-art methods like Gaussian Processes Regression or Additive Groves of Regression Trees. The training and optimization of Random Model Trees scales better than Gaussian Processes Regression to larger datasets, and enjoys a constant advantage over Additive Groves of the order of one to two orders of magnitude.