From f98d00d03ff6c59df6cf866fcfca764386ff3120 Mon Sep 17 00:00:00 2001 From: James Lamb Date: Sun, 12 Apr 2020 21:45:23 -0500 Subject: [PATCH] [R-package] Updated package metadata in DESCRIPTION --- R-package/DESCRIPTION | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION index 6720c418389b..55df4a779eb3 100755 --- a/R-package/DESCRIPTION +++ b/R-package/DESCRIPTION @@ -4,11 +4,11 @@ Title: Light Gradient Boosting Machine Version: 2.3.2 Date: 2019-11-26 Authors@R: c( - person("Guolin", "Ke", email = "guolin.ke@microsoft.com", role = c("aut", "cre")), - person("Damien", "Soukhavong", email = "damien.soukhavong@skema.edu", role = c("ctb")), - person("Yachen", "Yan", role = c("ctb")), - person("James", "Lamb", email="jaylamb20@gmail.com", role = c("ctb")) - ) + person("Guolin", "Ke", email = "guolin.ke@microsoft.com", role = c("aut", "cre")), + person("Damien", "Soukhavong", email = "damien.soukhavong@skema.edu", role = c("ctb")), + person("Yachen", "Yan", role = c("ctb")), + person("James", "Lamb", email="jaylamb20@gmail.com", role = c("ctb")) + ) Description: Tree based algorithms can be improved by introducing boosting frameworks. LightGBM is one such framework, and this package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. @@ -16,12 +16,14 @@ Description: Tree based algorithms can be improved by introducing boosting frame 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. - In recognition of these advantages, LightGBM has being widely-used in many winning solutions of machine learning competitions. + In recognition of these advantages, LightGBM has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, LightGBM can achieve a linear speed-up in training time by using multiple machines. Encoding: UTF-8 License: MIT + file LICENSE URL: https://github.com/Microsoft/LightGBM BugReports: https://github.com/Microsoft/LightGBM/issues +NeedsCompilation: yes +Biarch: false Suggests: ggplot2 (>= 1.0.1), knitr, @@ -37,4 +39,6 @@ Imports: Matrix (>= 1.1-0), methods, utils +SystemRequirements: + C++11 RoxygenNote: 7.0.2