# Practical considerations for variable screening in the super learner

arXiv: https://arxiv.org/abs/2311.03313 · HTML: https://mlsanity.com/paper/2311.03313/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Practical considerations for specifying a super learner](https://mlsanity.com/paper/2204.06139/) (2022)

## Abstract (arXiv)

Estimating a prediction function is a fundamental component of many data analyses. The super learner ensemble, a particular implementation of stacking, has desirable theoretical properties and has been used successfully in many applications. Dimension reduction can be accomplished by using variable screening algorithms (screeners), including the lasso, within the ensemble prior to fitting other prediction algorithms. However, the performance of a super learner using the lasso for dimension reduction has not been fully explored in cases where the lasso is known to perform poorly. We provide empirical results that suggest that a diverse set of candidate screeners should be used to protect against poor performance of any one screener, similar to the guidance for choosing a library of prediction algorithms for the super learner. These results are further illustrated through the analysis of HIV-1 antibody data.