Package: lightgbm 4.5.0

lightgbm: Light Gradient Boosting Machine

Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. 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.

Authors:Yu Shi [aut], Guolin Ke [aut], Damien Soukhavong [aut], James Lamb [aut, cre], Qi Meng [aut], Thomas Finley [aut], Taifeng Wang [aut], Wei Chen [aut], Weidong Ma [aut], Qiwei Ye [aut], Tie-Yan Liu [aut], Nikita Titov [aut], Yachen Yan [ctb], Microsoft Corporation [cph], Dropbox, Inc. [cph], Alberto Ferreira [ctb], Daniel Lemire [ctb], Victor Zverovich [cph], IBM Corporation [ctb], David Cortes [aut], Michael Mayer [ctb]

lightgbm_4.5.0.tar.gz


lightgbm_4.5.0.tar.gz(r-4.5-noble)lightgbm_4.5.0.tar.gz(r-4.4-noble)
lightgbm.pdf |lightgbm.html✨
lightgbm/json (API)

# Install 'lightgbm' in R:
install.packages('lightgbm', repos = c('https://jameslamb.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/microsoft/lightgbm/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

28 exports 1 stars 2.86 score 5 dependencies 4 dependents 10 mentions 1.3k scripts 9.3k downloads

Last updated 2 months agofrom:f9eaa7014f. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 26 2024
R-4.5-linux-x86_64NOTEAug 26 2024

Exports:get_fieldgetLGBMthreadslgb.configure_fast_predictlgb.convert_with_ruleslgb.cvlgb.Datasetlgb.Dataset.constructlgb.Dataset.create.validlgb.Dataset.savelgb.Dataset.set.categoricallgb.Dataset.set.referencelgb.drop_serializedlgb.dumplgb.get.eval.resultlgb.importancelgb.interpretelgb.loadlgb.make_serializablelgb.model.dt.treelgb.plot.importancelgb.plot.interpretationlgb.restore_handlelgb.savelgb.slice.Datasetlgb.trainlightgbmset_fieldsetLGBMthreads

Dependencies:data.tablejsonlitelatticeMatrixR6

Basic Walkthrough

Rendered frombasic_walkthrough.Rmdusingknitr::knitron Aug 26 2024.

Last update: 2024-01-19
Started: 2023-12-09

Readme and manuals

Help Manual

Help pageTopics
Test part from Mushroom Data Setagaricus.test
Training part from Mushroom Data Setagaricus.train
Bank Marketing Data Setbank
Dimensions of an 'lgb.Dataset'dim.lgb.Dataset
Handling of column names of 'lgb.Dataset'dimnames.lgb.Dataset dimnames<-.lgb.Dataset
Get one attribute of a 'lgb.Dataset'get_field get_field.lgb.Dataset
Get default number of threads used by LightGBMgetLGBMThreads getLGBMthreads
Configure Fast Single-Row Predictionslgb.configure_fast_predict
Data preparator for LightGBM datasets with rules (integer)lgb.convert_with_rules
Main CV logic for LightGBMlgb.cv
Construct 'lgb.Dataset' objectlgb.Dataset
Construct Dataset explicitlylgb.Dataset.construct
Construct validation datalgb.Dataset.create.valid
Save 'lgb.Dataset' to a binary filelgb.Dataset.save
Set categorical feature of 'lgb.Dataset'lgb.Dataset.set.categorical
Set reference of 'lgb.Dataset'lgb.Dataset.set.reference
Drop serialized raw bytes in a LightGBM model objectlgb.drop_serialized
Dump LightGBM model to jsonlgb.dump
Get record evaluation result from boosterlgb.get.eval.result
Compute feature importance in a modellgb.importance
Compute feature contribution of predictionlgb.interprete
Load LightGBM modellgb.load
Make a LightGBM object serializable by keeping raw byteslgb.make_serializable
Parse a LightGBM model json dumplgb.model.dt.tree
Plot feature importance as a bar graphlgb.plot.importance
Plot feature contribution as a bar graphlgb.plot.interpretation
Restore the C++ component of a de-serialized LightGBM modellgb.restore_handle
Save LightGBM modellgb.save
Slice a datasetlgb.slice.Dataset
Main training logic for LightGBMlgb.train
Train a LightGBM modellightgbm
Predict method for LightGBM modelpredict.lgb.Booster
Print method for LightGBM modelprint.lgb.Booster
Set one attribute of a 'lgb.Dataset' objectset_field set_field.lgb.Dataset
Set maximum number of threads used by LightGBMsetLGBMThreads setLGBMthreads
Summary method for LightGBM modelsummary.lgb.Booster