InfiniteBoost: building infinite ensembles with gradient descent
This work addresses the challenge of creating scalable and robust ensemble methods for machine learning practitioners, though it appears incremental by combining existing approaches.
The authors tackled the problem of building ensembles that avoid overfitting while incorporating error correction, resulting in InfiniteBoost, which demonstrated performance on regression, classification, and ranking tasks using large-scale datasets.
In machine learning ensemble methods have demonstrated high accuracy for the variety of problems in different areas. Two notable ensemble methods widely used in practice are gradient boosting and random forests. In this paper we present InfiniteBoost - a novel algorithm, which combines important properties of these two approaches. The algorithm constructs the ensemble of trees for which two properties hold: trees of the ensemble incorporate the mistakes done by others; at the same time the ensemble could contain the infinite number of trees without the over-fitting effect. The proposed algorithm is evaluated on the regression, classification, and ranking tasks using large scale, publicly available datasets.