Discretization and Grouping for Logistic Regression

A Stochastic-Expectation-Maximization (SEM) algorithm (Celeux et al. (1995) < https://inria.hal.science/inria-00074164>) associated with a Gibbs sampler which purpose is to learn a constrained representation for logistic regression that is called quantization (Ehrhardt et al. (2019) ). Continuous features are discretized and categorical features' values are grouped to produce a better logistic regression model. Pairwise interactions between the discretized and grouped features are dynamically added to the model through a Metropolis-Hastings algorithm (Hastings, W. K. (1970) ).