R package glmdisc: Discretization and Grouping for Logistic Regression. A Stochastic-Expectation-Maximization (SEM) algorithm (Celeux et al. (1995) <>) associated with a Gibbs sampler which purpose is to learn a constrained representation for logistic regression that is called quantization (Ehrhardt et al. (2019) <arXiv:1903.08920>). Continuous features are discretized and categorical features’ values are grouped to produce a better logistic regression model. Pairwise interactions between quantized features are dynamically added to the model through a Metropolis-Hastings algorithm (Hastings, W. K. (1970) <doi:10.1093/biomet/57.1.97>).

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  1. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv