Condensed-gradient boosting
Seyedsaman Emami, Gonzalo Martínez-Muñoz
Abstract
Abstract This paper presents a computationally efficient variant of Gradient Boosting (GB) for multi-class classification and multi-output regression tasks. Standard GB uses a 1-vs-all strategy for classification tasks with more than two classes. This strategy entails that one tree per class and iteration has to be trained. In this work, we propose the use of multi-output regressors as base models to handle the multi-class problem as a single task. In addition, the proposed modification allows the model to learn multi-output regression problems. An extensive comparison with other multi-output based Gradient Boosting methods is carried out in terms of generalization and computational efficiency. The proposed method showed the best trade-off between generalization ability and training and prediction speeds. Furthermore, an analysis of space and time complexity was undertaken.