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glmdisc: Discretization and Grouping for Logistic Regression
A Stochastic-Expectation-Maximization (SEM) algorithm (Celeux et al. (1995) <https://hal.inria.fr/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) <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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Package details |
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| Author | Adrien Ehrhardt [aut, cre], Vincent Vandewalle [aut], Christophe Biernacki [ctb], Philippe Heinrich [ctb] |
| Maintainer | Adrien Ehrhardt <adrien.ehrhardt@centraliens-lille.org> |
| License | GPL (>= 2) |
| Version | 0.6 |
| URL | https://adimajo.github.io |
| Package repository | View on CRAN |
| Installation |
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