Neural-NGBoost: Natural gradient boosting with neural network base learners

Jamshidjon Ganiev, Deok-Woong Kim, Seung-Hwan Bae

Abstract: NGBoost has shown promising results in probabilistic and point estimation tasks. However, it is vague still whether this method can be scalable to neural architecture system since its base learner is based on decision trees. To resolve this, we design a Neural-NGBoost framework by replacing the base learner with lightweight neural networks and introducing joint gradient estimation for boosting procedure. Based on natural gradient boosting, we iteratively update the neural based learner by inferring natural gradient and update the parameter score with its probabilistic distribution. Experimental results show Neural-NGBoost achieves superior performance across various datasets compared to other boosting methods.

External IDs:doi:10.1016/j.icte.2025.08.003