# xgb.dump

0th

Percentile

##### Save xgboost model to text file

Save a xgboost model to text file. Could be parsed later.

##### Usage
xgb.dump(model = NULL, fname = NULL, fmap = "", with_stats = FALSE,
dump_format = c("text", "json"), ...)
##### Arguments
model

the model object.

fname

the name of the text file where to save the model text dump. If not provided or set to NULL the function will return the model as a character vector.

fmap

feature map file representing the type of feature. Detailed description could be found at https://github.com/dmlc/xgboost/wiki/Binary-Classification#dump-model. See demo/ for walkthrough example in R, and https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt for example Format.

with_stats

whether dump statistics of splits When this option is on, the model dump comes with two additional statistics: gain is the approximate loss function gain we get in each split; cover is the sum of second order gradient in each node.

dump_format

either 'text' or 'json' format could be specified.

...

currently not used

##### Value

if fname is not provided or set to NULL the function will return the model as a character vector. Otherwise it will return TRUE.

• xgb.dump
##### Examples
# NOT RUN {
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
# save the model in file 'xgb.model.dump'
xgb.dump(bst, 'xgb.model.dump', with_stats = TRUE)

# print the model without saving it to a file
print(xgb.dump(bst, with_stats = TRUE))

# print in JSON format:
cat(xgb.dump(bst, with_stats = TRUE, dump_format='json'))

# }