compile Rd files, i.e. R documents

R-package/man/xgboost.Rd

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+% 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]))
+}
+