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Wording improvement

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This commit is contained in:
Michaël Benesty 2015-12-08 18:18:51 +01:00
parent ccd4b4be00
commit fbf2707561
6 changed files with 14 additions and 20 deletions
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2
R-package/R/xgb.create.features.R
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@ -14,7 +14,7 @@
#' @details
#' This is the function inspired from the paragraph 3.1 of the paper:
#'
#' \strong{"Practical Lessons from Predicting Clicks on Ads at Facebook"}
#' \strong{Practical Lessons from Predicting Clicks on Ads at Facebook}
#'
#' \emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers,
#' Joaquin Quiñonero Candela)}

13
R-package/R/xgb.importance.R
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@ -21,7 +21,7 @@
#' @details
#' This is the function to understand the model trained (and through your model, your data).
#'
#' Results are returned for both linear and tree models.
#' This function is for both linear and tree models.
#'
#' \code{data.table} is returned by the function.
#' The columns are :
@ -32,8 +32,9 @@
#' \item \code{Weight} percentage representing the relative number of times a feature have been taken into trees.
#' }
#'
#' If you don't provide name, index of the features are used.
#' They are extracted from the boost dump (made on the C++ side), the index starts at 0 (usual in C++) instead of 1 (usual in R).
#' If you don't provide \code{feature_names}, index of the features will be used instead.
#'
#' Because the index is extracted from the model dump (made on the C++ side), it starts at 0 (usual in C++) instead of 1 (usual in R).
#'
#' Co-occurence count
#' ------------------
@ -47,10 +48,6 @@
#' @examples
#' data(agaricus.train, package='xgboost')
#'
#' # Both dataset are list with two items, a sparse matrix and labels
#' # (labels = outcome column which will be learned).
#' # Each column of the sparse Matrix is a feature in one hot encoding format.
#'
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#'
@ -114,8 +111,6 @@ xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, labe
result
}
# Avoid error messages during CRAN check.
# The reason is that these variables are never declared
# They are mainly column names inferred by Data.table...

3
R-package/R/xgb.plot.deepness.R
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@ -76,6 +76,7 @@ get.paths.to.leaf <- function(dt.tree) {
#' @details
#' Display both the number of \code{leaf} and the distribution of \code{weighted observations}
#' by tree deepness level.
#'
#' The purpose of this function is to help the user to find the best trade-off to set
#' the \code{max.depth} and \code{min_child_weight} parameters according to the bias / variance trade-off.
#'
@ -88,7 +89,7 @@ get.paths.to.leaf <- function(dt.tree) {
#' \item Weighted cover: noramlized weighted cover per Leaf (weighted number of instances).
#' }
#'
#' This function is inspired by this blog post \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}
#' This function is inspired by the blog post \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}
#'
#' @examples
#' data(agaricus.train, package='xgboost')

2
R-package/man/xgb.create.features.Rd
View File

@ -20,7 +20,7 @@ May improve the learning by adding new features to the training data based on th
\details{
This is the function inspired from the paragraph 3.1 of the paper:
\strong{"Practical Lessons from Predicting Clicks on Ads at Facebook"}
\strong{Practical Lessons from Predicting Clicks on Ads at Facebook}
\emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers,
Joaquin Quiñonero Candela)}

11
R-package/man/xgb.importance.Rd
View File

@ -27,7 +27,7 @@ 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).
Results are returned for both linear and tree models.
This function is for both linear and tree models.
\code{data.table} is returned by the function.
The columns are :
@ -38,8 +38,9 @@ The columns are :
\item \code{Weight} percentage representing the relative number of times a feature have been taken into trees.
}
If you don't provide name, index of the features are used.
They are extracted from the boost dump (made on the C++ side), the index starts at 0 (usual in C++) instead of 1 (usual in R).
If you don't provide \code{feature_names}, index of the features will be used instead.
Because the index is extracted from the model dump (made on the C++ side), it starts at 0 (usual in C++) instead of 1 (usual in R).
Co-occurence count
------------------
@ -53,10 +54,6 @@ If you need to remember one thing only: until you want to leave us early, don't
\examples{
data(agaricus.train, package='xgboost')
# Both dataset are list with two items, a sparse matrix and labels
# (labels = outcome column which will be learned).
# Each column of the sparse Matrix is a feature in one hot encoding format.
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")

3
R-package/man/xgb.plot.deepness.Rd
View File

@ -18,6 +18,7 @@ Generate a graph to plot the distribution of deepness among trees.
\details{
Display both the number of \code{leaf} and the distribution of \code{weighted observations}
by tree deepness level.
The purpose of this function is to help the user to find the best trade-off to set
the \code{max.depth} and \code{min_child_weight} parameters according to the bias / variance trade-off.
@ -30,7 +31,7 @@ The graph is made of two parts:
\item Weighted cover: noramlized weighted cover per Leaf (weighted number of instances).
}
This function is inspired by this blog post \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}
This function is inspired by the blog post \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}
}
\examples{
data(agaricus.train, package='xgboost')