dblr: Discrete Boosting Logistic Regression

Trains logistic regression model by discretizing continuous variables via gradient boosting approach. The proposed method tries to achieve a tradeoff between interpretation and prediction accuracy for logistic regression by discretizing the continuous variables. The variable binning is accomplished in a supervised fashion. The model trained by this package is still a single logistic regression model, but not a sequence of logistic regression models. The fitted model object returned from the model training consists of two tables. One table is used to give the boundaries of bins for each continuous variable as well as the corresponding coefficients, and the other one is used for discrete variables. This package can also be used for binning continuous variables for other statistical analysis.

Version: 0.1.0
Imports: data.table (≥ 1.9.6), xgboost (≥ 0.6-4), CatEncoders (≥ 0.1.1), Metrics (≥ 0.1.1), methods
Published: 2017-10-11
DOI: 10.32614/CRAN.package.dblr
Author: Nailong Zhang
Maintainer: Nailong Zhang <setseed2016 at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: dblr results


Reference manual: dblr.pdf


Package source: dblr_0.1.0.tar.gz
Windows binaries: r-devel: dblr_0.1.0.zip, r-release: dblr_0.1.0.zip, r-oldrel: dblr_0.1.0.zip
macOS binaries: r-release (arm64): dblr_0.1.0.tgz, r-oldrel (arm64): dblr_0.1.0.tgz, r-release (x86_64): dblr_0.1.0.tgz, r-oldrel (x86_64): dblr_0.1.0.tgz


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