33 lines
1.5 KiB
R
33 lines
1.5 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/callbacks.R
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\name{xgb.cb.cv.predict}
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\alias{xgb.cb.cv.predict}
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\title{Callback for returning cross-validation based predictions}
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\usage{
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xgb.cb.cv.predict(save_models = FALSE, outputmargin = FALSE)
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}
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\arguments{
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\item{save_models}{A flag for whether to save the folds' models.}
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\item{outputmargin}{Whether to save margin predictions (same effect as passing this
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parameter to \link{predict.xgb.Booster}).}
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}
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\value{
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An \code{xgb.Callback} object, which can be passed to \code{\link[=xgb.cv]{xgb.cv()}},
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but \strong{not} to \code{\link[=xgb.train]{xgb.train()}}.
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}
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\description{
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This callback function saves predictions for all of the test folds,
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and also allows to save the folds' models.
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}
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\details{
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Predictions are saved inside of the \code{pred} element, which is either a vector or a matrix,
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depending on the number of prediction outputs per data row. The order of predictions corresponds
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to the order of rows in the original dataset. Note that when a custom \code{folds} list is
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provided in \code{\link[=xgb.cv]{xgb.cv()}}, the predictions would only be returned properly when this list is a
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non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be
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meaningful when user-provided folds have overlapping indices as in, e.g., random sampling splits.
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When some of the indices in the training dataset are not included into user-provided \code{folds},
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their prediction value would be \code{NA}.
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}
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