Light Gradient Boosting Machine
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Documentation for package ‘lightgbm’ version 4.3.0
- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- 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' |
| 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.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 |
| slice | Slice a dataset |
| slice.lgb.Dataset | Slice a dataset |
| summary.lgb.Booster | Summary method for LightGBM model |