On Clustering Categories of Categorical Predictors in Generalized Linear Models

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Abstract

We propose a method to reduce the complexity of Generalized Linear Models in the presence of categorical predictors. The traditional one-hot encoding, where each category is represented by a dummy variable, can be wasteful, difficult to interpret, and prone to overfitting, especially when dealing with high-cardinality categorical predictors. This paper addresses these challenges by finding a reduced representation of the categorical predictors by clustering their categories. This is done through a numerical method which aims to preserve (or even, improve) accuracy, while reducing the number of coefficients to be estimated for the categorical predictors. Thanks to its design, we are able to derive a proximity measure between categories of a categorical predictor that can be easily visualized. We illustrate the performance of our approach in real-world classification and count-data datasets where we see that clustering the categorical predictors reduces complexity substantially without harming accuracy.
Original languageEnglish
Article number115245
JournalExpert Systems with Applications
Number of pages16
ISSN0957-4174
DOIs
Publication statusPublished - 24 May 2021

Bibliographical note

E-pub ahead of print. Published online: 24 May 2021

Keywords

  • Statistical Learning
  • Interpretability
  • Greedy Randomized Adaptive Search Procedure
  • Proximity between categories

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