Dimensions of xgb.DMatrix
Set information of an xgb.DMatrix object
Parse a boosted tree model text dump
Extract gblinear coefficients history.
Accessors for model parameters as JSON string.
Accessors for model parameters.
Dump an xgboost model in text format.
Save xgb.DMatrix object to binary file
Accessors for serializable attributes of a model.
Get a new DMatrix containing the specified rows of
original xgb.DMatrix object
Create new features from a previously learned model
Cross Validation
SHAP contribution dependency plots
Project all trees on one tree and plot it
Importance of features in a model.
Construct xgb.DMatrix object
Plot feature importance as a bar graph
Restore missing parts of an incomplete xgb.Booster object.
SHAP contribution dependency summary plot
Plot model trees deepness
xgb.set.config, xgb.get.config
Set and get global configuration
Plot a boosted tree model
eXtreme Gradient Boosting Training
Load the instance back from xgb.serialize
Load xgboost model from binary file
Load serialised xgboost model from R's raw vector
Save xgboost model to R's raw vector,
user can call xgb.load.raw to load the model back from raw vector
Save xgboost model to binary file
Deprecation notices.
Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc.
Internal utility function.
Serialize the booster instance into R's raw vector. The serialization method differs
from xgb.save.raw
as the latter one saves only the model but not
parameters. This serialization format is not stable across different xgboost versions.
Callback closure for printing the result of evaluation
Training part from Mushroom Data Set
Callback closure for logging the evaluation history
a-compatibility-note-for-saveRDS-save
Do not use saveRDS
or save
for long-term archival of
models. Instead, use xgb.save
or xgb.save.raw
. Callback closures for booster training.
Callback closure for returning cross-validation based predictions.
Callback closure for collecting the model coefficients history of a gblinear booster
during its training.
Callback closure to activate the early stopping.
Callback closure for resetting the booster's parameters at each iteration.
Handling of column names of xgb.DMatrix
Callback closure for saving a model file.
Print xgb.Booster
Get information of an xgb.DMatrix object
Combine and melt feature values and SHAP contributions for sample
observations.
Predict method for eXtreme Gradient Boosting model
Test part from Mushroom Data Set
Scale feature value to have mean 0, standard deviation 1
Print xgb.cv result
Print xgb.DMatrix