% Generated by roxygen2 (4.0.1): do not edit by hand
\name{xgboost}
\alias{xgboost}
\title{eXtreme Gradient Boosting (Tree) library}
\usage{
xgboost(data = NULL, label = 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,}

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

  See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
  further details. See also demo/demo.R 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 xgboost in R
}
\details{
This is the modeling function for xgboost.

Parallelization is automatically enabled if OpenMP is present.
Number of threads can also be manually specified via "nthread" parameter
}
\examples{
data(iris)
bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
pred <- predict(bst, as.matrix(iris[,1:4]))
}