person("Guolin", "Ke", email = "guolin.ke@microsoft.com", role = c("aut", "cre")),
person("Guolin", "Ke", email = "guolin.ke@microsoft.com", role = c("aut", "cre")),
person("Damien", "Soukhavong", email = "damien.soukhavong@skema.edu", role = c("ctb")),
person("Damien", "Soukhavong", email = "damien.soukhavong@skema.edu", role = c("ctb")),
person("Yachen", "Yan", role = c("ctb")),
person("Yachen", "Yan", role = c("ctb")),
person("James", "Lamb", email="jaylamb20@gmail.com", 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.
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:
It is designed to be distributed and efficient with the following advantages:
1. Faster training speed and higher efficiency.
1. Faster training speed and higher efficiency.
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@@ -16,12 +16,14 @@ Description: Tree based algorithms can be improved by introducing boosting frame
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@@ -16,12 +16,14 @@ Description: Tree based algorithms can be improved by introducing boosting frame
3. Better accuracy.
3. Better accuracy.
4. Parallel learning supported.
4. Parallel learning supported.
5. Capable of handling large-scale data.
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.
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.
