  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 $O_p(n^{-1/4})$ and offers extensive empirical
evidence that it is an excellent candidate to substitute stacking or for the
analysis of Hierarchical problems.
