Random feature weights for regression trees

Ensembles are learning methods the operation of which relies on a combination of different base models. The diversity of ensembles is a fundamental aspect that conditions their operation. Random Feature Weights (RFW) was proposed as a classification-tree ensemble construction method in which diversity is introduced into each tree by means of a random weight associated with each attribute. These weights vary from one tree to another in the ensemble. In this article, the idea of RFW is adapted to decision-tree regression. A comparison is drawn with other ensemble construction methods: Bagging, Random Forest, Iterated Bagging, Random Subspaces and AdaBoost.R2 obtaining competitive results.