Light Gradient Boosting Machine

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Documentation for package ‘lightgbm’ version 2.0.7

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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.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.prepare Data preparator for LightGBM datasets (numeric)
lgb.prepare2 Data preparator for LightGBM datasets (integer)
lgb.prepare_rules Data preparator for LightGBM datasets with rules (numeric)
lgb.prepare_rules2 Data preparator for LightGBM datasets with rules (integer)
lgb.save Save LightGBM model
lgb.train Main CV logic for LightGBM
lgb.unloader LightGBM unloading error fix
lightgbm Main CV logic for LightGBM
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