agaricus.test | Test part from Mushroom Data Set |
agaricus.train | Training part from Mushroom Data Set |
bank | Bank Marketing Data Set |
dim | Dimensions of an 'lgb.Dataset' |
dimnames.lgb.Dataset | Handling of column names of 'lgb.Dataset' |
get_field | Get one attribute of a 'lgb.Dataset' |
getLGBMThreads | Get default number of threads used by LightGBM |
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_shared_dataset_params | Shared Dataset parameter docs |
lgb_shared_params | Shared parameter docs |
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 |
set_field | Set one attribute of a 'lgb.Dataset' object |
setLGBMThreads | Set maximum number of threads used by LightGBM |
summary.lgb.Booster | Summary method for LightGBM model |
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