% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/xgboost.R
\name{xgboost}
\alias{xgboost}
\title{eXtreme Gradient Boosting (Tree) library}
\usage{
xgboost(data = NULL, label = NULL, missing = NULL, params = list(),
  nrounds, verbose = 1, ...)
}
\arguments{
\item{data}{takes \code{matrix}, \code{dgCMatrix}, local data file or
\code{xgb.DMatrix}.}

\item{label}{the response variable. User should not set this field,
if data is local data file or  \code{xgb.DMatrix}.}

\item{missing}{Missing is only used when input is dense matrix, pick a float
value that represents missing value. Sometimes a data use 0 or other extreme value to represents missing values.}

\item{params}{the list of parameters.

Commonly used ones are:
\itemize{
  \item \code{objective} objective function, common ones are
  \itemize{
    \item \code{reg:linear} linear regression
    \item \code{binary:logistic} logistic regression for classification
  }
  \item \code{eta} step size of each boosting step
  \item \code{max.depth} maximum depth of the tree
  \item \code{nthread} number of thread used in training, if not set, all threads are used
}

  Look at \code{\link{xgb.train}} for a more complete list of parameters or \url{https://github.com/tqchen/xgboost/wiki/Parameters} for the full list.

  See also \code{demo/} for walkthrough example in R.}

\item{nrounds}{the max number of iterations}

\item{verbose}{If 0, xgboost will stay silent. If 1, xgboost will print
information of performance. If 2, xgboost will print information of both
performance and construction progress information}

\item{...}{other parameters to pass to \code{params}.}
}
\description{
A simple interface for training xgboost model. Look at \code{\link{xgb.train}} function for a more advanced interface.
}
\details{
This is the modeling function for Xgboost.

Parallelization is automatically enabled if \code{OpenMP} is present.

Number of threads can also be manually specified via \code{nthread} parameter.
}
\examples{
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, nround = 2, objective = "binary:logistic")
pred <- predict(bst, test$data)
}