Condensed Gradient Boosting

TL;DR

This paper introduces a more efficient gradient boosting method that handles multi-class and multi-output tasks simultaneously using multi-output regressors, improving speed and generalization.

Contribution

It proposes a novel multi-output gradient boosting approach that reduces computational complexity and enhances performance over existing methods.

Findings

Achieves better trade-off between accuracy and speed.

Outperforms other multi-output gradient boosting methods.

Demonstrates improved generalization in experiments.

Abstract

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.