From 4e1002a52c63f418c2178d3df4456c2e0bbbf30e Mon Sep 17 00:00:00 2001 From: pommedeterresautee Date: Tue, 14 Apr 2015 00:30:55 +0200 Subject: [PATCH] Experimental parameter --- R-package/R/xgb.train.R | 2 +- R-package/man/xgb.train.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R index 1444964e5..20908863f 100644 --- a/R-package/R/xgb.train.R +++ b/R-package/R/xgb.train.R @@ -22,7 +22,7 @@ #' \item \code{min_child_weight} minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. Default: 1 #' \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. Default: 1 #' \item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1 -#' \item \code{num_parallel_tree} number of trees to grow per round. Useful to test Random Forest through Xgboost (set \code{colsample_bytree < 1}, \code{subsample < 1} and \code{round = 1}) accordingly. Default: 1 +#' \item \code{num_parallel_tree} Experimental parameter. number of trees to grow per round. Useful to test Random Forest through Xgboost (set \code{colsample_bytree < 1}, \code{subsample < 1} and \code{round = 1}) accordingly. Default: 1 #' } #' #' 2.2. Parameter for Linear Booster diff --git a/R-package/man/xgb.train.Rd b/R-package/man/xgb.train.Rd index 91e21b50c..3f93b3989 100644 --- a/R-package/man/xgb.train.Rd +++ b/R-package/man/xgb.train.Rd @@ -28,7 +28,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL, \item \code{min_child_weight} minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. Default: 1 \item \code{subsample} subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. Default: 1 \item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1 - \item \code{num_parallel_tree} number of trees to grow per round. Useful to test Random Forest through Xgboost (set \code{colsample_bytree < 1}, \code{subsample < 1} and \code{round = 1}) accordingly. Default: 1 + \item \code{num_parallel_tree} Experimental parameter. number of trees to grow per round. Useful to test Random Forest through Xgboost (set \code{colsample_bytree < 1}, \code{subsample < 1} and \code{round = 1}) accordingly. Default: 1 } 2.2. Parameter for Linear Booster