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Improve predict function documentation

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pommedeterresautee 2015-12-02 15:47:12 +01:00
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11
R-package/R/predict.xgb.Booster.R
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@ -20,6 +20,17 @@ setClass("xgb.Booster",
#' only valid for gbtree, but not for gblinear. set it to be value bigger #' only valid for gbtree, but not for gblinear. set it to be value bigger
#' than 0. It will use all trees by default. #' than 0. It will use all trees by default.
#' @param predleaf whether predict leaf index instead. If set to TRUE, the output will be a matrix object. #' @param predleaf whether predict leaf index instead. If set to TRUE, the output will be a matrix object.
#'
#' @details
#' The option \code{ntreelimit} purpose is to let the user train a model with lots
#' of trees but use only the first trees for prediction to avoid overfitting
#' (without having to train a new model with less trees).
#'
#' The option \code{predleaf} purpose is inspired from §3.1 of the paper
#' \code{Practical Lessons from Predicting Clicks on Ads at Facebook}.
#' The idea is to use the model as a generator of new features which capture non linear link
#' from original features.
#'
#' @examples #' @examples
#' data(agaricus.train, package='xgboost') #' data(agaricus.train, package='xgboost')
#' data(agaricus.test, package='xgboost') #' data(agaricus.test, package='xgboost')

10
R-package/man/predict-xgb.Booster-method.Rd
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@ -31,6 +31,16 @@ than 0. It will use all trees by default.}
\description{ \description{
Predicted values based on xgboost model object. Predicted values based on xgboost model object.
} }
\details{
The option \code{ntreelimit} purpose is to let the user train a model with lots
of trees but use only the first trees for prediction to avoid overfitting
(without having to train a new model with less trees).
The option \code{predleaf} purpose is inspired from §3.1 of the paper
\code{Practical Lessons from Predicting Clicks on Ads at Facebook}.
The idea is to use the model as a generator of new features which capture non linear link
from original features.
}
\examples{ \examples{
data(agaricus.train, package='xgboost') data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost') data(agaricus.test, package='xgboost')