Sufficient Representations for Categorical Variables
Machine Learning
2021-10-29 v3 Machine Learning
Abstract
Many learning algorithms require categorical data to be transformed into real vectors before it can be used as input. Often, categorical variables are encoded as one-hot (or dummy) vectors. However, this mode of representation can be wasteful since it adds many low-signal regressors, especially when the number of unique categories is large. In this paper, we investigate simple alternative solutions for universally consistent estimators that rely on lower-dimensional real-valued representations of categorical variables that are "sufficient" in the sense that no predictive information is lost. We then compare preexisting and proposed methods on simulated and observational datasets.
Cite
@article{arxiv.1908.09874,
title = {Sufficient Representations for Categorical Variables},
author = {Jonathan Johannemann and Vitor Hadad and Susan Athey and Stefan Wager},
journal= {arXiv preprint arXiv:1908.09874},
year = {2021}
}