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lightgbm
View on CRAN: Click
here
Download and install lightgbm package within the R console
Install from CRAN:
install.packages("lightgbm")
Install from Github:
library("remotes")
install_github("cran/lightgbm")
Install by package version:
library("remotes")
install_version("lightgbm", "4.6.0")
Attach the package and use:
library("lightgbm")
Maintained by
James Lamb
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision . 10.32614/CRAN.package.lightgbm . lightgbm results . lightgbm.pdf . lightgbm_4.6.0.tar.gz . lightgbm_4.6.0.zip . lightgbm_4.6.0.zip . lightgbm_4.6.0.zip . lightgbm_4.6.0.tgz . lightgbm_4.6.0.tgz . lightgbm_4.6.0.tgz . lightgbm_4.6.0.tgz . lightgbm archive . https://CRAN.R-project.org/package=lightgbm .
First Published: 2020-09-21
Latest Update: 2024-01-18
Description:
Tree based algorithms can be improved by introducing boosting frameworks.
'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) .
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.
2. Lower memory usage.
3. Better accuracy.
4. Parallel learning supported.
5. Capable of handling large-scale data.
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.
How to cite:
James Lamb (2020). lightgbm: Light Gradient Boosting Machine. R package version 4.6.0, https://cran.r-project.org/web/packages/lightgbm. Accessed 03 May. 2025.
Previous versions and publish date:
3.0.0.2 (2020-10-01 10:30), 3.0.0 (2020-09-21 10:40), 3.1.0 (2020-11-19 00:20), 3.1.1 (2020-12-08 08:00), 3.2.0 (2021-03-22 23:30), 3.2.1 (2021-04-13 07:10), 3.3.0 (2021-10-09 17:00), 3.3.1 (2021-10-30 14:00), 3.3.2 (2022-01-14 14:12), 3.3.3 (2022-10-10 18:30), 3.3.4 (2022-12-16 08:50), 3.3.5 (2023-01-16 20:00), 4.2.0 (2023-12-08 12:10), 4.3.0 (2024-01-18 12:50), 4.4.0 (2024-06-15 07:20), 4.5.0 (2024-07-26 08:00)
Other packages that cited lightgbm R package
View lightgbm citation profile
Other R packages that lightgbm depends,
imports, suggests or enhances
Complete documentation for lightgbm
Functions, R codes and Examples using
the lightgbm R package
Some associated functions: agaricus.test . agaricus.train . bank . dim . dimnames.lgb.Dataset . getLGBMThreads . get_field . lgb.Dataset.construct . lgb.Dataset.create.valid . lgb.Dataset . lgb.Dataset.save . lgb.Dataset.set.categorical . lgb.Dataset.set.reference . lgb.configure_fast_predict . lgb.convert_with_rules . lgb.cv . lgb.drop_serialized . lgb.dump . lgb.get.eval.result . lgb.importance . lgb.interprete . lgb.load . lgb.make_serializable . lgb.model.dt.tree . lgb.plot.importance . lgb.plot.interpretation . lgb.restore_handle . lgb.save . lgb.train . lgb_shared_dataset_params . lgb_shared_params . lightgbm . predict.lgb.Booster . print.lgb.Booster . setLGBMThreads . set_field . slice . summary.lgb.Booster .
Some associated R codes: aliases.R . callback.R . lgb.Booster.R . lgb.DataProcessor.R . lgb.Dataset.R . lgb.Predictor.R . lgb.convert_with_rules.R . lgb.cv.R . lgb.drop_serialized.R . lgb.importance.R . lgb.interprete.R . lgb.make_serializable.R . lgb.model.dt.tree.R . lgb.plot.importance.R . lgb.plot.interpretation.R . lgb.restore_handle.R . lgb.train.R . lightgbm.R . metrics.R . multithreading.R . utils.R . Full lightgbm package functions and examples
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