cb.cv.predict |
Callback closure for returning cross-validation based predictions. |
getinfo |
Get information of an xgb.DMatrix object |
xgb.attr |
Accessors for serializable attributes of a model. |
xgb.DMatrix.save |
Save xgb.DMatrix object to binary file |
dimnames.xgb.DMatrix |
Handling of column names of xgb.DMatrix |
cb.save.model |
Callback closure for saving a model file. |
cb.early.stop |
Callback closure to activate the early stopping. |
slice |
Get a new DMatrix containing the specified rows of
original xgb.DMatrix object |
dim.xgb.DMatrix |
Dimensions of xgb.DMatrix |
setinfo |
Set information of an xgb.DMatrix object |
cb.evaluation.log |
Callback closure for logging the evaluation history |
xgb.load |
Load xgboost model from binary file |
xgb.gblinear.history |
Extract gblinear coefficients history. |
prepare.ggplot.shap.data |
Combine and melt feature values and SHAP contributions for sample
observations. |
xgb.DMatrix |
Construct xgb.DMatrix object |
xgb.Booster.complete |
Restore missing parts of an incomplete xgb.Booster object. |
xgb.ggplot.deepness |
Plot model trees deepness |
xgboost-deprecated |
Deprecation notices. |
xgb.ggplot.importance |
Plot feature importance as a bar graph |
xgb.config |
Accessors for model parameters as JSON string. |
xgb.create.features |
Create new features from a previously learned model |
xgb.model.dt.tree |
Parse a boosted tree model text dump |
print.xgb.Booster |
Print xgb.Booster |
xgb.save |
Save xgboost model to binary file |
xgb.save.raw |
Save xgboost model to R's raw vector,
user can call xgb.load.raw to load the model back from raw vector |
xgb.load.raw |
Load serialised xgboost model from R's raw vector |
normalize |
Scale feature value to have mean 0, standard deviation 1 |
predict.xgb.Booster |
Predict method for eXtreme Gradient Boosting model |
cb.gblinear.history |
Callback closure for collecting the model coefficients history of a gblinear booster
during its training. |
xgb.importance |
Importance of features in a model. |
xgb.train |
eXtreme Gradient Boosting Training |
xgb.parameters<- |
Accessors for model parameters. |
xgb.cv |
Cross Validation |
xgb.unserialize |
Load the instance back from xgb.serialize |
xgb.ggplot.shap.summary |
SHAP contribution dependency summary plot |
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.dump |
Dump an xgboost model in text format. |
xgb.shap.data |
Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc.
Internal utility function. |
xgb.serialize |
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. |
agaricus.test |
Test part from Mushroom Data Set |
callbacks |
Callback closures for booster 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. |
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. |
agaricus.train |
Training part from Mushroom Data Set |
print.xgb.DMatrix |
Print xgb.DMatrix |
print.xgb.cv.synchronous |
Print xgb.cv result |
No Results! |