% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/xgb.cv.R
\name{xgb.cv}
\alias{xgb.cv}
\title{Cross Validation}
\usage{
xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
  missing = NULL, showsd = TRUE, metrics = list(), obj = NULL,
  feval = NULL, verbose = T, ...)
}
\arguments{
\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
}

  See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
  further details. See also demo/ for walkthrough example in R.}

\item{data}{takes an \code{xgb.DMatrix} as the input.}

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

\item{nfold}{number of folds used}

\item{label}{option field, when data is Matrix}

\item{missing}{Missing is only used when input is dense matrix, pick a float}

\item{showsd}{\code{boolean}, whether show standard deviation of cross validation}

\item{metrics,}{list of evaluation metrics to be used in corss validation,
  when it is not specified, the evaluation metric is chosen according to objective function.
  Possible options are:
\itemize{
  \item \code{error} binary classification error rate
  \item \code{rmse} Rooted mean square error
  \item \code{logloss} negative log-likelihood function
  \item \code{auc} Area under curve
  \item \code{merror} Exact matching error, used to evaluate multi-class classification
}}

\item{obj}{customized objective function. Returns gradient and second order
gradient with given prediction and dtrain,}

\item{feval}{custimized evaluation function. Returns
\code{list(metric='metric-name', value='metric-value')} with given
prediction and dtrain,}

\item{verbose}{\code{boolean}, print the statistics during the process.}

\item{...}{other parameters to pass to \code{params}.}
}
\value{
A \code{data.table} with each mean and standard deviation stat for training set and test set.
}
\description{
The cross valudation function of xgboost
}
\details{
This is the cross validation function for xgboost

Parallelization is automatically enabled if OpenMP is present.
Number of threads can also be manually specified via "nthread" parameter.

This function only accepts an \code{xgb.DMatrix} object as the input.
}
\examples{
data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
                  "max.depth"=3, "eta"=1, "objective"="binary:logistic")
print(history)
}