# A Survey Of Regression Algorithms And Connections With Deep Learning

arXiv: https://arxiv.org/abs/2104.12647 · HTML: https://mlsanity.com/paper/2104.12647/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 89 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [Adaptive Huber Regression](https://mlsanity.com/paper/1706.06991/) (2017)

## Built on by (most cited first, 1 of 1)

- [Local MDI+: Local Feature Importances for Tree-Based Models](https://mlsanity.com/paper/2506.08928/) (2025)

## Abstract (arXiv)

Regression has attracted immense interest lately due to its effectiveness in tasks like predicting values. And Regression is of widespread use in multiple fields such as Economics, Finance, Business, Biology and so on. While considerable studies have proposed some impressive models, few of them have provided a whole picture regarding how and to what extent Regression has developed. With the aim of aiding beginners in understanding the relationships among different Regression algorithms, this paper characterizes a broad and thoughtful selection of recent regression algorithms, providing an organized and comprehensive overview of existing work and models utilized frequently. In this paper, the relationship between Regression and Deep Learning is also discussed and a conclusion can be drawn that Deep Learning can be more powerful as an combination with Regression models in the future.