# Light Gradient Boosting Machine

## Documentation for package ‘lightgbm’ version 3.2.1

- DESCRIPTION file.
- Code demos. Use demo() to run them.

## 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'
getinfo | Get information of an 'lgb.Dataset' object
getinfo.lgb.Dataset | Get information of an 'lgb.Dataset' object
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.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.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.save | Save LightGBM model
lgb.train | Main training logic for LightGBM
lgb.unloader | Remove lightgbm and its objects from an environment
lightgbm | Train a LightGBM model
predict.lgb.Booster | Predict method for LightGBM model
readRDS.lgb.Booster | readRDS for 'lgb.Booster' models
saveRDS.lgb.Booster | saveRDS for 'lgb.Booster' models
setinfo | Set information of an 'lgb.Dataset' object
setinfo.lgb.Dataset | Set information of an 'lgb.Dataset' object
slice | Slice a dataset
slice.lgb.Dataset | Slice a dataset