% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lgb.Dataset.R
\name{lgb.Dataset}
\alias{lgb.Dataset}
\title{Construct \code{lgb.Dataset} object}
\usage{
lgb.Dataset(
  data,
  params = list(),
  reference = NULL,
  colnames = NULL,
  categorical_feature = NULL,
  free_raw_data = TRUE,
  info = list(),
  ...
)
}
\arguments{
\item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename}

\item{params}{a list of parameters. See
\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{
The "Dataset Parameters" section of the documentation} for a list of parameters
and valid values.}

\item{reference}{reference dataset. When LightGBM creates a Dataset, it does some preprocessing like binning
continuous features into histograms. If you want to apply the same bin boundaries from an existing
dataset to new \code{data}, pass that existing Dataset to this argument.}

\item{colnames}{names of columns}

\item{categorical_feature}{categorical features. This can either be a character vector of feature
names or an integer vector with the indices of the features (e.g.
\code{c(1L, 10L)} to say "the first and tenth columns").}

\item{free_raw_data}{LightGBM constructs its data format, called a "Dataset", from tabular data.
By default, that Dataset object on the R side does not keep a copy of the raw data.
This reduces LightGBM's memory consumption, but it means that the Dataset object
cannot be changed after it has been constructed. If you'd prefer to be able to
change the Dataset object after construction, set \code{free_raw_data = FALSE}.}

\item{info}{a list of information of the \code{lgb.Dataset} object}

\item{...}{other information to pass to \code{info} or parameters pass to \code{params}}
}
\value{
constructed dataset
}
\description{
Construct \code{lgb.Dataset} object from dense matrix, sparse matrix
             or local file (that was created previously by saving an \code{lgb.Dataset}).
}
\examples{
\donttest{
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
data_file <- tempfile(fileext = ".data")
lgb.Dataset.save(dtrain, data_file)
dtrain <- lgb.Dataset(data_file)
lgb.Dataset.construct(dtrain)
}
}