Combining Tree-Boosting with Gaussian Process and Mixed Effects
Models
![[R logo]](../../../doc/html/Rlogo.svg)
![[Up]](../../../doc/html/left.jpg)
![[Top]](../../../doc/html/up.jpg)
Documentation for package ‘gpboost’ version 1.4.0.1
- 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 |
| coords | Example data for the GPBoost package |
| coords_test | Example data for the GPBoost package |
| dim.gpb.Dataset | Dimensions of an 'gpb.Dataset' |
| dimnames.gpb.Dataset | Handling of column names of 'gpb.Dataset' |
| dimnames<-.gpb.Dataset | Handling of column names of 'gpb.Dataset' |
| fit | Generic 'fit' method for a 'GPModel' |
| fit.GPModel | Fits a 'GPModel' |
| fitGPModel | Fits a 'GPModel' |
| getinfo | Get information of an 'gpb.Dataset' object |
| getinfo.gpb.Dataset | Get information of an 'gpb.Dataset' object |
| get_nested_categories | Auxiliary function to create categorical variables for nested grouped random effects |
| gpb.convert_with_rules | Data preparator for GPBoost datasets with rules (integer) |
| gpb.cv | CV function for number of boosting iterations |
| gpb.Dataset | Construct 'gpb.Dataset' object |
| gpb.Dataset.construct | Construct Dataset explicitly |
| gpb.Dataset.create.valid | Construct validation data |
| gpb.Dataset.save | Save 'gpb.Dataset' to a binary file |
| gpb.Dataset.set.categorical | Set categorical feature of 'gpb.Dataset' |
| gpb.Dataset.set.reference | Set reference of 'gpb.Dataset' |
| gpb.dump | Dump GPBoost model to json |
| gpb.get.eval.result | Get record evaluation result from booster |
| gpb.grid.search.tune.parameters | Function for choosing tuning parameters |
| gpb.importance | Compute feature importance in a model |
| gpb.interprete | Compute feature contribution of prediction |
| gpb.load | Load GPBoost model |
| gpb.model.dt.tree | Parse a GPBoost model json dump |
| gpb.plot.importance | Plot feature importance as a bar graph |
| gpb.plot.interpretation | Plot feature contribution as a bar graph |
| gpb.plot.part.dep.interact | Plot interaction partial dependence plots |
| gpb.plot.partial.dependence | Plot partial dependence plots |
| gpb.save | Save GPBoost model |
| gpb.train | Main training logic for GBPoost |
| gpboost | Train a GPBoost model |
| GPBoost_data | Example data for the GPBoost package |
| GPModel | Create a 'GPModel' object |
| GPModel_shared_params | Documentation for parameters shared by 'GPModel', 'gpb.cv', and 'gpboost' |
| group_data | Example data for the GPBoost package |
| group_data_test | Example data for the GPBoost package |
| loadGPModel | Load a 'GPModel' from a file |
| neg_log_likelihood | Evaluate the negative log-likelihood |
| neg_log_likelihood.GPModel | Evaluate the negative log-likelihood |
| predict.gpb.Booster | Prediction function for 'gpb.Booster' objects |
| predict.GPModel | Make predictions for a 'GPModel' |
| predict_training_data_random_effects | Predict ("estimate") training data random effects for a 'GPModel' |
| predict_training_data_random_effects.GPModel | Predict ("estimate") training data random effects for a 'GPModel' |
| readRDS.gpb.Booster | readRDS for 'gpb.Booster' models |
| saveGPModel | Save a 'GPModel' |
| saveRDS.gpb.Booster | saveRDS for 'gpb.Booster' models |
| setinfo | Set information of an 'gpb.Dataset' object |
| setinfo.gpb.Dataset | Set information of an 'gpb.Dataset' object |
| set_optim_params | Set parameters for estimation of the covariance parameters |
| set_optim_params.GPModel | Set parameters for estimation of the covariance parameters |
| set_prediction_data | Set prediction data for a 'GPModel' |
| set_prediction_data.GPModel | Set prediction data for a 'GPModel' |
| slice | Slice a dataset |
| slice.gpb.Dataset | Slice a dataset |
| summary.GPModel | Summary for a 'GPModel' |
| X | Example data for the GPBoost package |
| X_test | Example data for the GPBoost package |
| y | Example data for the GPBoost package |