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% Generated by roxygen2: 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 = NA,
  prediction = FALSE, showsd = TRUE, metrics = list(), obj = NULL,
  feval = NULL, stratified = TRUE, folds = NULL, verbose = TRUE,
  print_every_n = 1L, early_stopping_rounds = NULL, maximize = NULL,
  callbacks = list(), ...)
}
\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 \code{\link{xgb.train}} for further details.
  See also demo/ for walkthrough example in R.}

\item{data}{takes an \code{xgb.DMatrix}, \code{matrix}, or \code{dgCMatrix} as the input.}

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

\item{nfold}{the original dataset is randomly partitioned into \code{nfold} equal size subsamples.}

\item{label}{vector of response values. Should be provided only when data is an R-matrix.}

\item{missing}{is only used when input is a dense matrix. By default is set to NA, which means 
that NA values should be considered as 'missing' by the algorithm. 
Sometimes, 0 or other extreme value might be used to represent missing values.}

\item{prediction}{A logical value indicating whether to return the test fold predictions 
from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.}

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

\item{metrics, }{list of evaluation metrics to be used in cross 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.}