Extreme Gradient Boosting
Model Serialization and Compatibility
Extract coefficients from linear booster
Dimensions of xgb.DMatrix
Handling of column names of xgb.DMatrix
Get or set information of xgb.DMatrix and xgb.Booster objects
Predict method for XGBoost model
Compute predictions from XGBoost model on new data
Print xgb.Booster
Print xgb.cv result
Print xgb.DMatrix
Print info from XGBoost model
Get Features Names from Booster
Accessors for serializable attributes of a model
XGBoost Callback Constructor
Callback for returning cross-validation based predictions
Callback to activate early stopping
Callback for logging the evaluation history
Callback for collecting coefficients history of a gblinear booster
Callback for printing the result of evaluation
Callback for resetting booster parameters at each iteration
Callback for saving a model file
Accessors for model parameters as JSON string
Deep-copies a Booster Object
Create new features from a previously learned model
Cross Validation
Structure for Data Batches
XGBoost Data Iterator
Check whether DMatrix object has a field
Construct xgb.DMatrix object
Save xgb.DMatrix object to binary file
Dump an XGBoost model in text format.
DMatrix from External Data
Extract gblinear coefficients history
Get DMatrix Data
Get Number of Non-Missing Entries in DMatrix
Get Quantile Cuts from DMatrix
Get number of boosting in a fitted booster
Feature importance
Check if two boosters share the same C object
Load serialised XGBoost model from R's raw vector
Load XGBoost model from binary file
Parse model text dump
Accessors for model parameters
XGBoost Parameters
Plot model tree depth
Plot feature importance
Project all trees on one tree
SHAP dependence plots
SHAP summary plot
Plot boosted trees
QuantileDMatrix from External Data
Save XGBoost model to R's raw vector
Save XGBoost model to binary file
Slice Booster by Rounds
Slice DMatrix
Fit XGBoost Model
Set and get global configuration
XGBoost Options
Fit XGBoost Model
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.