compile Rd files, i.e. R documents

R-package/man/xgb.train.Rd

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+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{xgb.train}
+\alias{xgb.train}
+\title{eXtreme Gradient Boosting Training}
+\usage{
+xgb.train(params = list(), dtrain, nrounds, watchlist = list(),
+  obj = NULL, feval = NULL, ...)
+}
+\arguments{
+\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{dtrain}{takes an \code{xgb.DMatrix} as the input.}
+
+\item{nrounds}{the max number of iterations}
+
+\item{watchlist}{what information should be printed when \code{verbose=1} or
+  \code{verbose=2}. Watchlist is used to specify validation set monitoring
+  during training. For example user can specify
+   watchlist=list(validation1=mat1, validation2=mat2) to watch
+   the performance of each round's model on mat1 and mat2}
+
+\item{obj}{customized objective function. Given prediction and dtrain,
+return gradient and second order gradient.}
+
+\item{feval}{custimized evaluation function. Given prediction and dtrain,
+return a \code{list(metric='metric-name', value='metric-value')}.}
+
+\item{...}{other parameters to pass to \code{params}.}
+}
+\description{
+The training function of xgboost
+}
+\details{
+This is the training 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.
+It supports advanced features such as watchlist, customized objective function,
+therefore it is more flexible than \code{\link{xgboost}}.
+}
+\examples{
+data(iris)
+iris[,5] <- as.numeric(iris[,5])
+dtrain = xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+dtest = dtrain
+watchlist <- list(eval = dtest, train = dtrain)
+param <- list(max_depth = 2, eta = 1, silent = 1)
+logregobj <- function(preds, dtrain) {
+   labels <- getinfo(dtrain, "label")
+   preds <- 1/(1 + exp(-preds))
+   grad <- preds - labels
+   hess <- preds * (1 - preds)
+   return(list(grad = grad, hess = hess))
+}
+evalerror <- function(preds, dtrain) {
+  labels <- getinfo(dtrain, "label")
+  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
+  return(list(metric = "error", value = err))
+}
+bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror)
+}
+