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wording fix

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This commit is contained in:
Michaël Benesty 2015-12-10 11:33:40 +01:00
parent b2e68b8dc7
commit 1b07f86eb8
2 changed files with 4 additions and 8 deletions
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6
R-package/R/xgb.importance.R
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@ -10,8 +10,8 @@
#' @importFrom Matrix cBind
#' @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 model generated by the \code{xgb.train} function. Avoid the creation of a dump file.
#' @param feature_names names of each feature as a \code{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 model generated by the \code{xgb.train} function.
#' @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.
@ -19,8 +19,6 @@
#' @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.
#'
#' @details
#' This is the function to understand the model trained (and through your model, your data).
#'
#' This function is for both linear and tree models.
#'
#' \code{data.table} is returned by the function.

6
R-package/man/xgb.importance.Rd
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@ -8,9 +8,9 @@ xgb.importance(feature_names = NULL, model = NULL, data = NULL,
label = NULL, target = function(x) ((x + label) == 2))
}
\arguments{
\item{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}.}
\item{feature_names}{names of each feature as a \code{character} vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.}
\item{model}{generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
\item{model}{generated by the \code{xgb.train} function.}
\item{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.}
@ -25,8 +25,6 @@ A \code{data.table} of the features used in the model with their average gain (a
Create a \code{data.table} of the most important features of a model.
}
\details{
This is the function to understand the model trained (and through your model, your data).
This function is for both linear and tree models.
\code{data.table} is returned by the function.