61 lines
2.1 KiB
R
61 lines
2.1 KiB
R
% Generated by roxygen2 (4.0.1): do not edit by hand
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\name{xgboost}
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\alias{xgboost}
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\title{eXtreme Gradient Boosting (Tree) library}
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\usage{
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xgboost(data = NULL, label = NULL, missing = NULL, params = list(),
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nrounds, verbose = 1, ...)
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}
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\arguments{
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\item{data}{takes \code{matrix}, \code{dgCMatrix}, local data file or
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\code{xgb.DMatrix}.}
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\item{label}{the response variable. User should not set this field,
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if data is local data file or \code{xgb.DMatrix}.}
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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{nrounds}{the max number of iterations}
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\item{verbose}{If 0, xgboost will stay silent. If 1, xgboost will print
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information of performance. If 2, xgboost will print information of both
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performance and construction progress information}
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\item{missing}{Missing is only used when input is dense matrix, pick a float
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value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.}
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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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A simple interface for xgboost in R
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}
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\details{
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This is the modeling 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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}
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\examples{
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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train <- agaricus.train
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test <- agaricus.test
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 1, nround = 2,objective = "binary:logistic")
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pred <- predict(bst, test$data)
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}
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