# Feature Encodings for Gradient Boosting with Automunge

**Authors:** Nicholas J. Teague  
**arXiv:** [2209.12309](https://arxiv.org/abs/2209.12309)  
**Date:** 2022-09-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2209.12309v1/

## Abstract

Selecting a default feature encoding strategy for gradient boosted learning may consider metrics of training duration and achieved predictive performance associated with the feature representations. The Automunge library for dataframe preprocessing offers a default of binarization for categoric features and z-score normalization for numeric. The presented study sought to validate those defaults by way of benchmarking on a series of diverse data sets by encoding variations with tuned gradient boosted learning. We found that on average our chosen defaults were top performers both from a tuning duration and a model performance standpoint. Another key finding was that one hot encoding did not perform in a manner consistent with suitability to serve as a categoric default in comparison to categoric binarization. We present here these and further benchmarks.