Other packages > Find by keyword >

lightgbm  

Light Gradient Boosting Machine
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", "4.5.0")



Attach the package and use:
library("lightgbm")
Maintained by
James Lamb
[Scholar Profile | Author Map]
All associated links for this package
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 4.5.0, https://cran.r-project.org/web/packages/lightgbm. Accessed 27 Jan. 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)
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
Downloads during the last 30 days
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

shinybrms  
Graphical User Interface ('shiny' App) for 'brms'
A graphical user interface (GUI) for fitting Bayesian regression models using the package 'brms' wh ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
intccr  
Semiparametric Competing Risks Regression under Interval Censoring
Semiparametric regression models on the cumulative incidence function for interval-censored competin ...
Download / Learn more Package Citations See dependency  
mgm  
Estimating Time-Varying k-Order Mixed Graphical Models
Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net re ...
Download / Learn more Package Citations See dependency  
AssetCorr  
Estimating Asset Correlations from Default Data
Functions for the estimation of intra- and inter-cohort correlations in the Vasicek credit portfolio ...
Download / Learn more Package Citations See dependency  

23,580

R Packages

204,057

Dependencies

63,980

Author Associations

23,561

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA