Wording improvement

R-package/R/xgb.create.features.R

@@ -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)}

R-package/R/xgb.importance.R

@@ -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...

R-package/R/xgb.plot.deepness.R

@@ -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')