# Feature Selection Tutorial with Python Examples

arXiv: https://arxiv.org/abs/2106.06437 · HTML: https://mlsanity.com/paper/2106.06437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 11 Jun 2021.

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

- [Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining](https://mlsanity.com/paper/1711.08477/) (2017)

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

- [Correlation Based Feature Subset Selection for Multivariate Time-Series Data](https://mlsanity.com/paper/2112.03705/) (2021)

## Abstract (arXiv)

In Machine Learning, feature selection entails selecting a subset of the available features in a dataset to use for model development. There are many motivations for feature selection, it may result in better models, it may provide insight into the data and it may deliver economies in data gathering or data processing. For these reasons feature selection has received a lot of attention in data analytics research. In this paper we provide an overview of the main methods and present practical examples with Python implementations. While the main focus is on supervised feature selection techniques, we also cover some feature transformation methods.