% Generated by roxygen2 (4.1.1): 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, weight = NULL,
  params = list(), nrounds, verbose = 1, print.every.n = 1L,
  early.stop.round = NULL, maximize = NULL, save_period = 0,
  save_name = "xgboost.model", ...)
}
\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{weight}{a vector indicating the weight for each row of the input.}

\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/dmlc/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{print.every.n}{Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.}

\item{early.stop.round}{If \code{NULL}, the early stopping function is not triggered.
If set to an integer \code{k}, training with a validation set will stop if the performance
keeps getting worse consecutively for \code{k} rounds.}

\item{maximize}{If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
\code{maximize=TRUE} means the larger the evaluation score the better.}

\item{save_period}{save the model to the disk in every \code{save_period} rounds, 0 means no such action.}

\item{save_name}{the name or path for periodically saved model file.}

\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)
}

