# Relational Boosted Regression Trees

arXiv: https://arxiv.org/abs/2107.12373 · HTML: https://mlsanity.com/paper/2107.12373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 25 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Sketching as a Tool for Numerical Linear Algebra](https://mlsanity.com/paper/1411.4357/) (2014)
- [FAQ: Questions Asked Frequently](https://mlsanity.com/paper/1504.04044/) (2015)
- [Size bounds and query plans for relational joins](https://mlsanity.com/paper/1711.03860/) (2017)
- [AC/DC: In-Database Learning Thunderstruck](https://mlsanity.com/paper/1803.07480/) (2018)
- [Relational Algorithms for k-means Clustering](https://mlsanity.com/paper/2008.00358/) (2020)
- [In-Database Regression in Input Sparsity Time](https://mlsanity.com/paper/2107.05672/) (2021)
- [A Relational Gradient Descent Algorithm For Support Vector Machine Training](https://mlsanity.com/paper/2005.05325/) (2020)

## Abstract (arXiv)

Many tasks use data housed in relational databases to train boosted regression tree models. In this paper, we give a relational adaptation of the greedy algorithm for training boosted regression trees. For the subproblem of calculating the sum of squared residuals of the dataset, which dominates the runtime of the boosting algorithm, we provide a (1 + ε)-approximation using the tensor sketch technique. Employing this approximation within the relational boosted regression trees algorithm leads to learning similar model parameters, but with asymptotically better runtime.