From 8233d589b64a7c487d8413cc032ce921789cc7f7 Mon Sep 17 00:00:00 2001
From: pommedeterresautee <Salutation33>
Date: Wed, 2 Dec 2015 15:47:12 +0100
Subject: [PATCH] Improve predict function documentation

---
 R-package/R/predict.xgb.Booster.R           | 11 +++++++++++
 R-package/man/predict-xgb.Booster-method.Rd | 10 ++++++++++
 2 files changed, 21 insertions(+)

diff --git a/R-package/R/predict.xgb.Booster.R b/R-package/R/predict.xgb.Booster.R
index abdb94e75..d608f3465 100644
--- a/R-package/R/predict.xgb.Booster.R
+++ b/R-package/R/predict.xgb.Booster.R
@@ -20,6 +20,17 @@ setClass("xgb.Booster",
 #'  only valid for gbtree, but not for gblinear. set it to be value bigger 
 #'  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.
+#' 
+#' @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
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
diff --git a/R-package/man/predict-xgb.Booster-method.Rd b/R-package/man/predict-xgb.Booster-method.Rd
index 13f37802e..341ced8c6 100644
--- a/R-package/man/predict-xgb.Booster-method.Rd
+++ b/R-package/man/predict-xgb.Booster-method.Rd
@@ -31,6 +31,16 @@ than 0. It will use all trees by default.}
 \description{
 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{
 data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')