R: Light Gradient Boosting Machine

# Light Gradient Boosting Machine

## Documentation for package ‘lightgbm’ version 4.6.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Code demos. Use demo() to run them.
- Package README

## Help Pages

agaricus.test | Test part from Mushroom Data Set
agaricus.train | Training part from Mushroom Data Set
bank | Bank Marketing Data Set
dim.lgb.Dataset | Dimensions of an 'lgb.Dataset'
dimnames.lgb.Dataset | Handling of column names of 'lgb.Dataset'
dimnames<-.lgb.Dataset | Handling of column names of 'lgb.Dataset'
getLGBMThreads | Get default number of threads used by LightGBM
getLGBMthreads | Get default number of threads used by LightGBM
get_field | Get one attribute of a 'lgb.Dataset'
get_field.lgb.Dataset | Get one attribute of a 'lgb.Dataset'
lgb.configure_fast_predict | Configure Fast Single-Row Predictions
lgb.convert_with_rules | Data preparator for LightGBM datasets with rules (integer)
lgb.cv | Main CV logic for LightGBM
lgb.Dataset | Construct 'lgb.Dataset' object
lgb.Dataset.construct | Construct Dataset explicitly
lgb.Dataset.create.valid | Construct validation data
lgb.Dataset.save | Save 'lgb.Dataset' to a binary file
lgb.Dataset.set.categorical | Set categorical feature of 'lgb.Dataset'
lgb.Dataset.set.reference | Set reference of 'lgb.Dataset'
lgb.drop_serialized | Drop serialized raw bytes in a LightGBM model object
lgb.dump | Dump LightGBM model to json
lgb.get.eval.result | Get record evaluation result from booster
lgb.importance | Compute feature importance in a model
lgb.interprete | Compute feature contribution of prediction
lgb.load | Load LightGBM model
lgb.make_serializable | Make a LightGBM object serializable by keeping raw bytes
lgb.model.dt.tree | Parse a LightGBM model json dump
lgb.plot.importance | Plot feature importance as a bar graph
lgb.plot.interpretation | Plot feature contribution as a bar graph
lgb.restore_handle | Restore the C++ component of a de-serialized LightGBM model
lgb.save | Save LightGBM model
lgb.slice.Dataset | Slice a dataset
lgb.train | Main training logic for LightGBM
lightgbm | Train a LightGBM model
predict.lgb.Booster | Predict method for LightGBM model
print.lgb.Booster | Print method for LightGBM model
setLGBMThreads | Set maximum number of threads used by LightGBM
setLGBMthreads | Set maximum number of threads used by LightGBM
set_field | Set one attribute of a 'lgb.Dataset' object
set_field.lgb.Dataset | Set one attribute of a 'lgb.Dataset' object
summary.lgb.Booster | Summary method for LightGBM model