Learn Together Stop Apart: An Inclusive Approach to Ensemble Pruning

Gradient Boosting is a leading learning method that builds ensembles and adapts their sizes to particular tasks, consistently delivering top-tier results across various applications. However, determining the optimal number of models in the ensemble remains a critical yet underexplored aspect. Traditional approaches assume a universal ensemble size effective for all data points, which may not always hold true due to data heterogeneity.