# What is to be gained by ensemble models in analysis of spectroscopic data?

arXiv: https://arxiv.org/abs/2404.02184 · HTML: https://mlsanity.com/paper/2404.02184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [bartMachine: Machine Learning with Bayesian Additive Regression Trees](https://mlsanity.com/paper/1312.2171/) (2013)
- [On the cross-validation bias due to unsupervised pre-processing](https://mlsanity.com/paper/1901.08974/) (2019)
- [Comparison of Machine Learning Models in Food Authentication Studies](https://mlsanity.com/paper/1905.07302/) (2019)
- [Generalizing Gain Penalization for Feature Selection in Tree-based Models](https://mlsanity.com/paper/2006.07515/) (2020)

## Abstract (arXiv)

An empirical study was carried out to compare different implementations of ensemble models aimed at improving prediction in spectroscopic data. A wide range of candidate models were fitted to benchmark datasets from regression and classification settings. A statistical analysis using linear mixed model was carried out on prediction performance criteria resulting from model fits over random splits of the data. The results showed that the ensemble classifiers were able to consistently outperform candidate models in our application