# Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles

arXiv: https://arxiv.org/abs/1902.07215 · HTML: https://mlsanity.com/paper/1902.07215/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions](https://mlsanity.com/paper/2312.14212/) (2023)
- [Robust Calibration For Improved Weather Prediction Under Distributional Shift](https://mlsanity.com/paper/2401.04144/) (2024)

## Abstract (arXiv)

An ever-looming threat to astronomical applications of machine learning is the danger of over-fitting data, also known as the `curse of dimensionality.' This occurs when there are fewer samples than the number of independent variables. In this work, we focus on the problem of stellar parameterization from low-mid resolution spectra, with blended absorption lines. We address this problem using an iterative algorithm to sequentially prune redundant features from synthetic PHOENIX spectra, and arrive at an optimal set of wavelengths with the strongest correlation with each of the output variables -- Trm eff, log g, and [Fe/H]. We find that at any given resolution, most features (i.e., absorption lines) are not only redundant, but actually act as noise and decrease the accuracy of parameter retrieval.