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Fix Rstat

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pommedeterresautee 2015-11-30 10:26:23 +01:00
parent 84ab71dd7e
commit c5dedeb318
2 changed files with 0 additions and 8 deletions
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6
R-package/R/xgb.importance.R
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@ -12,15 +12,9 @@
#' @importFrom Matrix sparseVector
#'
#' @param feature_names names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
#'
#' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).
#'
#' @param model generated by the \code{xgb.train} function. Avoid the creation of a dump file.
#'
#' @param data the dataset used for the training step. Will be used with \code{label} parameter for co-occurence computation. More information in \code{Detail} part. This parameter is optional.
#'
#' @param label the label vetor used for the training step. Will be used with \code{data} parameter for co-occurence computation. More information in \code{Detail} part. This parameter is optional.
#'
#' @param target a function which returns \code{TRUE} or \code{1} when an observation should be count as a co-occurence and \code{FALSE} or \code{0} otherwise. Default function is provided for computing co-occurences in a binary classification. The \code{target} function should have only one parameter. This parameter will be used to provide each important feature vector after having applied the split condition, therefore these vector will be only made of 0 and 1 only, whatever was the information before. More information in \code{Detail} part. This parameter is optional.
#'
#' @return A \code{data.table} of the features used in the model with their average gain (and their weight for boosted tree model) in the model.

2
R-package/man/xgb.importance.Rd
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@ -17,8 +17,6 @@ xgb.importance(feature_names = NULL, model = NULL, data = NULL,
\item{label}{the label vetor used for the training step. Will be used with \code{data} parameter for co-occurence computation. More information in \code{Detail} part. This parameter is optional.}
\item{target}{a function which returns \code{TRUE} or \code{1} when an observation should be count as a co-occurence and \code{FALSE} or \code{0} otherwise. Default function is provided for computing co-occurences in a binary classification. The \code{target} function should have only one parameter. This parameter will be used to provide each important feature vector after having applied the split condition, therefore these vector will be only made of 0 and 1 only, whatever was the information before. More information in \code{Detail} part. This parameter is optional.}
\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).}
}
\value{
A \code{data.table} of the features used in the model with their average gain (and their weight for boosted tree model) in the model.