Joint leaf-refinement and ensemble pruning through L1 regularization

Abstract

Ensembles are among the state-of-the-art in many machine learning applications. With the ongoing integration of ML models into everyday life, e.g., in the form of the Internet of Things, the deployment and continuous application of models become more and more an important issue. Therefore, small models that offer good predictive performance and use small amounts of memory are required. Ensemble pruning is a standard technique for removing unnecessary classifiers from a large ensemble that reduces the overall resource consumption and sometimes improves the performance of the original ensemble. Similarly, leaf-refinement is a technique that improves the performance of a tree ensemble by jointly re-learning the probability estimates in the leaf nodes of the trees, thereby allowing for smaller ensembles while preserving their predictive performance.