xgboost (version 0.3-1)

xgb.train: eXtreme Gradient Boosting Training

Description

The training function of xgboost

Usage

xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
  feval = NULL, verbose = 1, ...)

Arguments

params

the list of parameters. Commonly used ones are:

• objectiveobjective function, common ones are
  • reg:linearlinear regression
  • binary:logisticlogistic regression for classification

data

takes an xgb.DMatrix as the input.

nrounds

the max number of iterations

watchlist

what information should be printed when verbose=1 or verbose=2. Watchlist is used to specify validation set monitoring during training. For example user can specify watchlist=list(validation1=mat1, validation2=mat2) to wat

obj

customized objective function. Returns gradient and second order gradient with given prediction and dtrain,

feval

custimized evaluation function. Returns list(metric='metric-name', value='metric-value') with given prediction and dtrain,

verbose

If 0, xgboost will stay silent. If 1, xgboost will print information of performance. If 2, xgboost will print information of both

...

other parameters to pass to params.

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 xgb.DMatrix object as the input. It supports advanced features such as watchlist, customized objective function, therefore it is more flexible than xgboost.

Examples

data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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)

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