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Statistics > Machine Learning

arXiv:1908.09874 (stat)
[Submitted on 26 Aug 2019 (v1), last revised 28 Oct 2021 (this version, v3)]

Title:Sufficient Representations for Categorical Variables

Authors:Jonathan Johannemann, Vitor Hadad, Susan Athey, Stefan Wager
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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.
Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as: arXiv:1908.09874 [stat.ML]
  (or arXiv:1908.09874v3 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.1908.09874

Submission history

From: Vitor Hadad [view email]
[v1] Mon, 26 Aug 2019 18:41:29 UTC (2,316 KB)
[v2] Sat, 15 Feb 2020 20:28:32 UTC (2,317 KB)
[v3] Thu, 28 Oct 2021 17:56:28 UTC (2,669 KB)
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