In this article we consider the Conditional Super Learner (CSL), an algorithm which selects the best model candidate from a library conditional on the covariates. The CSL expands the idea of using cross-validation to select the best model and merges it with meta learning. Here we propose a specific algorithm that finds a local minimum to the problem posed, proof that it converges at a rate faster than Op(n−1/4) and offers extensive empirical evidence that it is an excellent candidate to substitute stacking or for the analysis of Hierarchical problems.
@article{arxiv.1912.06675,
title = {Conditional Super Learner},
author = {Gilmer Valdes and Yannet Interian and Efstathios D. Gennatas Mark J. Van der Laan},
journal= {arXiv preprint arXiv:1912.06675},
year = {2021}
}