xgb.save: Save XGBoost model to binary file

In xgboost: Extreme Gradient Boosting

Save XGBoost model to binary file

Description

Save XGBoost model to a file in binary or JSON format.

Usage

xgb.save(model, fname)

Arguments

model

Model object of xgb.Booster class.

fname

Name of the file to write. Its extension determines the serialization format: ".ubj": Use the universal binary JSON format (recommended). This format uses binary types for e.g. floating point numbers, thereby preventing any loss of precision when converting to a human-readable JSON text or similar. ".json": Use plain JSON, which is a human-readable format. ".deprecated": Use deprecated binary format. This format will not be able to save attributes introduced after v1 of XGBoost, such as the "best_iteration" attribute that boosters might keep, nor feature names or user-specifiec attributes. If the format is not specified by passing one of the file extensions above, will default to UBJ.

Details

This methods allows to save a model in an XGBoost-internal binary or text format which is universal among the various xgboost interfaces. In R, the saved model file could be read later using either the xgb.load() function or the xgb_model parameter of xgb.train().

Note: a model can also be saved as an R object (e.g., by using readRDS() or save()). However, it would then only be compatible with R, and corresponding R methods would need to be used to load it. Moreover, persisting the model with readRDS() or save() might cause compatibility problems in future versions of XGBoost. Consult a-compatibility-note-for-saveRDS-save to learn how to persist models in a future-proof way, i.e., to make the model accessible in future releases of XGBoost.

See Also

xgb.load()

Examples

data(agaricus.train, package = "xgboost")
data(agaricus.test, package = "xgboost")

## Keep the number of threads to 1 for examples
nthread <- 1
data.table::setDTthreads(nthread)

train <- agaricus.train
test <- agaricus.test

bst <- xgb.train(
  data = xgb.DMatrix(train$data, label = train$label, nthread = 1),
  nrounds = 2,
  params = xgb.params(
    max_depth = 2,
    nthread = nthread,
    objective = "binary:logistic"
  )
)

fname <- file.path(tempdir(), "xgb.ubj")
xgb.save(bst, fname)
bst <- xgb.load(fname)

xgboost documentation built on Dec. 3, 2025, 5:06 p.m.