xgb.DMatrix: Construct xgb.DMatrix object

In xgboost: Extreme Gradient Boosting

Construct xgb.DMatrix object

Description

Construct xgb.DMatrix object from either a dense matrix, a sparse matrix, or a local file. Supported input file formats are either a LIBSVM text file or a binary file that was created previously by xgb.DMatrix.save).

Usage

xgb.DMatrix(
  data,
  info = list(),
  missing = NA,
  silent = FALSE,
  nthread = NULL,
  ...
)

Arguments

data	a matrix object (either numeric or integer), a dgCMatrix object, a dgRMatrix object (only when making predictions from a fitted model), a dsparseVector object (only when making predictions from a fitted model, will be interpreted as a row vector), or a character string representing a filename.
info	a named list of additional information to store in the xgb.DMatrix object. See setinfo for the specific allowed kinds of
missing	a float value to represents missing values in data (used only when input is a dense matrix). It is useful when a 0 or some other extreme value represents missing values in data.
silent	whether to suppress printing an informational message after loading from a file.
nthread	Number of threads used for creating DMatrix.
...	the info data could be passed directly as parameters, without creating an info list.

Examples

data(agaricus.train, package='xgboost')
## Keep the number of threads to 1 for examples
nthread <- 1
data.table::setDTthreads(nthread)
dtrain <- with(
  agaricus.train, xgb.DMatrix(data, label = label, nthread = nthread)
)
xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
dtrain <- xgb.DMatrix('xgb.DMatrix.data')
if (file.exists('xgb.DMatrix.data')) file.remove('xgb.DMatrix.data')

xgboost documentation built on Sept. 11, 2024, 8:26 p.m.