glmdisc 0.1
Discretization and Grouping for Logistic Regression
Released Apr 4, 2019 by Adrien Ehrhardt
This package cannot yet be used with Renjin it depends on other packages which are not available:
caret 6.0-84
Dependencies
caret 6.0-84
gam 1.16
nnet 7.3-12
MASS 7.3-51.4
RcppEigen 0.3.3.5.0
RcppNumerical 0.3-2
Rcpp
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) ). 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) ).
