91 lines
3.6 KiB
R
91 lines
3.6 KiB
R
% Generated by roxygen2 (4.1.0): do not edit by hand
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% Please edit documentation in R/xgb.cv.R
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\name{xgb.cv}
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\alias{xgb.cv}
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\title{Cross Validation}
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\usage{
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xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
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missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
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obj = NULL, feval = NULL, stratified = TRUE, verbose = T, ...)
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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 \link{xgb.train} for further details.
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See also demo/ for walkthrough example in R.}
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\item{data}{takes an \code{xgb.DMatrix} or \code{Matrix} as the input.}
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\item{nrounds}{the max number of iterations}
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\item{nfold}{the original dataset is randomly partitioned into \code{nfold} equal size subsamples.}
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\item{label}{option field, when data is \code{Matrix}}
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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{prediction}{A logical value indicating whether to return the prediction vector.}
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\item{showsd}{\code{boolean}, whether show standard deviation of cross validation}
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\item{metrics,}{list of evaluation metrics to be used in corss validation,
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when it is not specified, the evaluation metric is chosen according to objective function.
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Possible options are:
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\itemize{
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\item \code{error} binary classification error rate
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\item \code{rmse} Rooted mean square error
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\item \code{logloss} negative log-likelihood function
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\item \code{auc} Area under curve
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\item \code{merror} Exact matching error, used to evaluate multi-class classification
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}}
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\item{obj}{customized objective function. Returns gradient and second order
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gradient with given prediction and dtrain.}
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\item{feval}{custimized evaluation function. Returns
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\code{list(metric='metric-name', value='metric-value')} with given
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prediction and dtrain.}
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\item{stratified}{\code{boolean}, whether the sampling of folds should be stratified by the values of labels in \code{data}}
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\item{verbose}{\code{boolean}, print the statistics during the process}
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\item{...}{other parameters to pass to \code{params}.}
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}
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\value{
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A \code{data.table} with each mean and standard deviation stat for training set and test set.
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}
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\description{
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The cross valudation function of xgboost
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}
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\details{
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The original sample is randomly partitioned into \code{nfold} equal size subsamples.
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Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
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The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
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All observations are used for both training and validation.
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Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
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}
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\examples{
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data(agaricus.train, package='xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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history <- xgb.cv(data = dtrain, nround=3, nthread = 2, nfold = 5, metrics=list("rmse","auc"),
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max.depth =3, eta = 1, objective = "binary:logistic")
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print(history)
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}
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