Condensed Gradient Boosting
This work addresses computational bottlenecks for practitioners using gradient boosting in multi-class classification and multi-output regression, though it is incremental as it builds on existing gradient boosting frameworks.
The paper tackles the computational inefficiency of standard gradient boosting in multi-class and multi-output tasks by proposing a variant that uses multi-output regressors as base models, resulting in the best trade-off between generalization ability and training/prediction speeds compared to other methods.
This paper presents a computationally efficient variant of gradient boosting for multi-class classification and multi-output regression tasks. Standard gradient boosting uses a 1-vs-all strategy for classifications tasks with more than two classes. This strategy translates in 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-ouptut 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 predictions speeds.