2b5b96d760
* [R] xgb.save must work when handle in nil but raw exists * [R] print.xgb.Booster should still print other info when handle is nil * [R] rename internal function xgb.Booster to xgb.Booster.handle to make its intent clear * [R] rename xgb.Booster.check to xgb.Booster.complete and make it visible; more docs * [R] storing evaluation_log should depend only on watchlist, not on verbose * [R] reduce the excessive chattiness of unit tests * [R] only disable some tests in windows when it's not 64-bit * [R] clean-up xgb.DMatrix * [R] test xgb.DMatrix loading from libsvm text file * [R] store feature_names in xgb.Booster, use them from utility functions * [R] remove non-functional co-occurence computation from xgb.importance * [R] verbose=0 is enough without a callback * [R] added forgotten xgb.Booster.complete.Rd; cran check fixes * [R] update installation instructions
42 lines
1.2 KiB
R
42 lines
1.2 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/xgb.load.R
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\name{xgb.load}
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\alias{xgb.load}
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\title{Load xgboost model from binary file}
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\usage{
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xgb.load(modelfile)
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}
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\arguments{
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\item{modelfile}{the name of the binary input file.}
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}
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\value{
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An object of \code{xgb.Booster} class.
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}
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\description{
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Load xgboost model from the binary model file.
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}
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\details{
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The input file is expected to contain a model saved in an xgboost-internal binary format
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using either \code{\link{xgb.save}} or \code{\link{cb.save.model}} in R, or using some
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appropriate methods from other xgboost interfaces. E.g., a model trained in Python and
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saved from there in xgboost format, could be loaded from R.
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Note: a model saved as an R-object, has to be loaded using corresponding R-methods,
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not \code{xgb.load}.
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}
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\examples{
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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train <- agaricus.train
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test <- agaricus.test
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bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
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eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
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xgb.save(bst, 'xgb.model')
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bst <- xgb.load('xgb.model')
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pred <- predict(bst, test$data)
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}
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\seealso{
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\code{\link{xgb.save}}, \code{\link{xgb.Booster.complete}}.
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}
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