73 lines
2.5 KiB
R
73 lines
2.5 KiB
R
% Generated by roxygen2 (4.0.1): do not edit by hand
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\name{xgb.cv}
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\alias{xgb.cv}
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\title{Cross Validation}
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\usage{
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xgb.cv(params = list(), data, nrounds, nfold, label = NULL, showsd = TRUE,
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metrics = list(), obj = NULL, feval = NULL, ...)
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}
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\arguments{
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\item{params}{the list of parameters. Commonly used ones are:
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\itemize{
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\item \code{objective} objective function, common ones are
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\itemize{
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\item \code{reg:linear} linear regression
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\item \code{binary:logistic} logistic regression for classification
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}
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\item \code{eta} step size of each boosting step
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\item \code{max_depth} maximum depth of the tree
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\item \code{nthread} number of thread used in training, if not set, all threads are used
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}
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See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
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further details. See also demo/ for walkthrough example in R.}
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\item{data}{takes an \code{xgb.DMatrix} as the input.}
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\item{nrounds}{the max number of iterations}
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\item{nfold}{number of folds used}
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\item{label}{option field, when data is Matrix}
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\item{showsd}{boolean, whether show standard deviation of cross validation}
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\item{metrics,}{list of evaluation metrics to be used in corss validation,
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when it is not specified, the evaluation metric is chosen according to objective function.
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Possible options are:
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\itemize{
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\item \code{error} binary classification error rate
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\item \code{rmse} Rooted mean square error
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\item \code{logloss} negative log-likelihood function
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\item \code{auc} Area under curve
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\item \code{merror} Exact matching error, used to evaluate multi-class classification
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}}
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\item{obj}{customized objective function. Returns gradient and second order
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gradient with given prediction and dtrain,}
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\item{feval}{custimized evaluation function. Returns
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\code{list(metric='metric-name', value='metric-value')} with given
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prediction and dtrain,}
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\item{...}{other parameters to pass to \code{params}.}
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}
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\description{
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The cross valudation function of xgboost
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}
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\details{
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This is the cross validation function for xgboost
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Parallelization is automatically enabled if OpenMP is present.
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Number of threads can also be manually specified via "nthread" parameter.
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This function only accepts an \code{xgb.DMatrix} object as the input.
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}
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\examples{
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data(agaricus.train, package='xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
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"max_depth"=3, "eta"=1, "objective"="binary:logistic")
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}
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