agaricus.test | Test part from Mushroom Data Set |
agaricus.train | Training part from Mushroom Data Set |
callbacks | Callback closures for booster training. |
cb.cv.predict | Callback closure for returning cross-validation based predictions. |
cb.early.stop | Callback closure to activate the early stopping. |
cb.evaluation.log | Callback closure for logging the evaluation history |
cb.gblinear.history | Callback closure for collecting the model coefficients history of a gblinear booster during its training. |
cb.print.evaluation | Callback closure for printing the result of evaluation |
cb.reset.parameters | Callback closure for resetting the booster's parameters at each iteration. |
cb.save.model | Callback closure for saving a model file. |
dim.xgb.DMatrix | Dimensions of xgb.DMatrix |
dimnames.xgb.DMatrix | Handling of column names of 'xgb.DMatrix' |
dimnames<-.xgb.DMatrix | Handling of column names of 'xgb.DMatrix' |
getinfo | Get information of an xgb.DMatrix object |
getinfo.xgb.DMatrix | Get information of an xgb.DMatrix object |
predict.xgb.Booster | Predict method for eXtreme Gradient Boosting model |
predict.xgb.Booster.handle | Predict method for eXtreme Gradient Boosting model |
print.xgb.Booster | Print xgb.Booster |
print.xgb.cv.synchronous | Print xgb.cv result |
print.xgb.DMatrix | Print xgb.DMatrix |
setinfo | Set information of an xgb.DMatrix object |
setinfo.xgb.DMatrix | Set information of an xgb.DMatrix object |
slice | Get a new DMatrix containing the specified rows of original xgb.DMatrix object |
slice.xgb.DMatrix | Get a new DMatrix containing the specified rows of original xgb.DMatrix object |
xgb.attr | Accessors for serializable attributes of a model. |
xgb.attr<- | Accessors for serializable attributes of a model. |
xgb.attributes | Accessors for serializable attributes of a model. |
xgb.attributes<- | Accessors for serializable attributes of a model. |
xgb.Booster.complete | Restore missing parts of an incomplete xgb.Booster object. |
xgb.create.features | Create new features from a previously learned model |
xgb.cv | Cross Validation |
xgb.DMatrix | Construct xgb.DMatrix object |
xgb.DMatrix.save | Save xgb.DMatrix object to binary file |
xgb.dump | Dump an xgboost model in text format. |
xgb.gblinear.history | Extract gblinear coefficients history. |
xgb.ggplot.deepness | Plot model trees deepness |
xgb.ggplot.importance | Plot feature importance as a bar graph |
xgb.importance | Importance of features in a model. |
xgb.load | Load xgboost model from binary file |
xgb.model.dt.tree | Parse a boosted tree model text dump |
xgb.parameters<- | Accessors for model parameters. |
xgb.plot.deepness | Plot model trees deepness |
xgb.plot.importance | Plot feature importance as a bar graph |
xgb.plot.multi.trees | Project all trees on one tree and plot it |
xgb.plot.shap | SHAP contribution dependency plots |
xgb.plot.tree | Plot a boosted tree model |
xgb.save | Save xgboost model to binary file |
xgb.save.raw | Save xgboost model to R's raw vector, user can call xgb.load to load the model back from raw vector |
xgb.train | eXtreme Gradient Boosting Training |
xgboost | eXtreme Gradient Boosting Training |
xgboost-deprecated | Deprecation notices. |
[.xgb.DMatrix | Get a new DMatrix containing the specified rows of original xgb.DMatrix object |