Extreme Gradient Boosting
Print xgb.cv result
Print xgb.DMatrix
Deprecation notices.
Do not use saveRDS or save for long-term archival of models. Inste...
Callback closures for booster training.
Callback closure for returning cross-validation based predictions.
Callback closure to activate the early stopping.
Callback closure for logging the evaluation history
Callback closure for collecting the model coefficients history of a gb...
Callback closure for printing the result of evaluation
Callback closure for resetting the booster's parameters at each iterat...
Callback closure for saving a model file.
Dimensions of xgb.DMatrix
Handling of column names of xgb.DMatrix
Get information of an xgb.DMatrix object
Scale feature value to have mean 0, standard deviation 1
Predict method for eXtreme Gradient Boosting model
Combine and melt feature values and SHAP contributions for sample obse...
Print xgb.Booster
Set information of an xgb.DMatrix object
Get a new DMatrix containing the specified rows of original xgb.DMatri...
Accessors for serializable attributes of a model.
Restore missing parts of an incomplete xgb.Booster object.
Accessors for model parameters as JSON string.
Create new features from a previously learned model
Cross Validation
Accessors for model parameters.
Construct xgb.DMatrix object
Save xgb.DMatrix object to binary file
Dump an xgboost model in text format.
Extract gblinear coefficients history.
Importance of features in a model.
Load serialised xgboost model from R's raw vector
Load xgboost model from binary file
Parse a boosted tree model text dump
Plot model trees deepness
Plot feature importance as a bar graph
Project all trees on one tree and plot it
SHAP contribution dependency plots
SHAP contribution dependency summary plot
Plot a boosted tree model
Save xgboost model to R's raw vector, user can call xgb.load.raw to lo...
Save xgboost model to binary file
Serialize the booster instance into R's raw vector. The serialization ...
Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.sha...
eXtreme Gradient Boosting Training
Load the instance back from xgb.serialize
Set and get global configuration
Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.