Sufficient Representations for Categorical Variables

Code

• github.com/grf-labs/sufrep
  OfficialIn papernone★ 0
• github.com/ischeinfeld/natality
  none★ 1
• github.com/atecon/CategoryEncoders
  none★ 0

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.

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