Reference

Datasets Datasets included with the R-package
agaricus.train	Training part from Mushroom Data Set
agaricus.test	Test part from Mushroom Data Set
bank	Bank Marketing Data Set
Data Input / Output Data I/O required for LightGBM
dim(<lgb.Dataset>)	Dimensions of an lgb.Dataset
dimnames(<lgb.Dataset>) `dimnames<-`(<lgb.Dataset>)	Handling of column names of lgb.Dataset
get_field()	Get one attribute of a lgb.Dataset
set_field()	Set one attribute of a lgb.Dataset object
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.convert_with_rules()	Data preparator for LightGBM datasets with rules (integer)
Machine Learning Train models with LightGBM and then use them to make predictions on new data
lightgbm()	Train a LightGBM model
lgb.train()	Main training logic for LightGBM
predict(<lgb.Booster>)	Predict method for LightGBM model
lgb.cv()	Main CV logic for LightGBM
Saving / Loading Models Save and load LightGBM models
lgb.dump()	Dump LightGBM model to json
lgb.save()	Save LightGBM model
lgb.load()	Load LightGBM model
lgb.model.dt.tree()	Parse a LightGBM model json dump
Model Interpretation Analyze your models
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.plot.importance()	Plot feature importance as a bar graph
lgb.plot.interpretation()	Plot feature contribution as a bar graph