# Generalized Linear Tree Space Nearest Neighbor

arXiv: https://arxiv.org/abs/2103.16408 · HTML: https://mlsanity.com/paper/2103.16408/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 30 Mar 2021.

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

- [CatBoost: unbiased boosting with categorical features](https://mlsanity.com/paper/1706.09516/) (2017)

## Abstract (arXiv)

We present a novel method of stacking decision trees by projection into an ordered time split out-of-fold (OOF) one nearest neighbor (1NN) space. The predictions of these one nearest neighbors are combined through a linear model. This process is repeated many times and averaged to reduce variance. Generalized Linear Tree Space Nearest Neighbor (GLTSNN) is competitive with respect to Mean Squared Error (MSE) compared to Random Forest (RF) on several publicly available datasets. Some of the theoretical and applied advantages of GLTSNN are discussed. We conjecture a classifier based upon the GLTSNN would have an error that is asymptotically bounded by twice the Bayes error rate like k = 1 Nearest Neighbor.