# Use of High Dimensional Modeling for automatic variables selection: the best path algorithm

arXiv: https://arxiv.org/abs/2105.03173 · HTML: https://mlsanity.com/paper/2105.03173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 88 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 4 of 4)

- [Best Subset Selection via a Modern Optimization Lens](https://mlsanity.com/paper/1507.03133/) (2015)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Challenges of Feature Selection for Big Data Analytics](https://mlsanity.com/paper/1611.01875/) (2016)
- [varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets](https://mlsanity.com/paper/1804.07134/) (2018)

## Abstract (arXiv)

This paper presents a new algorithm for automatic variables selection. In particular, using the Graphical Models properties it is possible to develop a method that can be used in the contest of large dataset. The advantage of this algorithm is that can be combined with different forecasting models. In this research we have used the OLS method and we have compared the result with the LASSO method.