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