From 4378f1f039566cc0f48e8e010b46fed332c95b97 Mon Sep 17 00:00:00 2001 From: Tianqi Chen Date: Fri, 22 Aug 2014 19:58:59 -0700 Subject: [PATCH 1/3] Update mushroom.conf --- demo/binary_classification/mushroom.conf | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/demo/binary_classification/mushroom.conf b/demo/binary_classification/mushroom.conf index dbc832244..d364905f7 100644 --- a/demo/binary_classification/mushroom.conf +++ b/demo/binary_classification/mushroom.conf @@ -1,6 +1,6 @@ # General Parameters, see comment for each definition -# choose the tree booster, 0: tree, 1: linear -booster_type = 0 +# choose the booster, can be gbtree or gblinear +booster = gbtree # choose logistic regression loss function for binary classification objective = binary:logistic From cf69d34d06ecd326f10ffa59557b977829fc055d Mon Sep 17 00:00:00 2001 From: Tianqi Chen Date: Fri, 22 Aug 2014 19:59:30 -0700 Subject: [PATCH 2/3] Update mq2008.conf --- demo/rank/mq2008.conf | 2 -- 1 file changed, 2 deletions(-) diff --git a/demo/rank/mq2008.conf b/demo/rank/mq2008.conf index 65ad19b8e..90aadec4e 100644 --- a/demo/rank/mq2008.conf +++ b/demo/rank/mq2008.conf @@ -1,6 +1,4 @@ # General Parameters, see comment for each definition -# choose the tree booster, 0: tree, 1: linear -booster_type = 0 # specify objective objective="rank:pairwise" From 13b52698553b4bf64f4679a761c89be99a19f97a Mon Sep 17 00:00:00 2001 From: Tianqi Chen Date: Fri, 22 Aug 2014 20:00:04 -0700 Subject: [PATCH 3/3] Update machine.conf --- demo/regression/machine.conf | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/demo/regression/machine.conf b/demo/regression/machine.conf index 5142bcbcf..f5a5163a8 100644 --- a/demo/regression/machine.conf +++ b/demo/regression/machine.conf @@ -1,6 +1,6 @@ # General Parameters, see comment for each definition -# choose the tree booster, 0: tree, 1: linear -booster_type = 0 +# choose the tree booster, can also change to gblinear +booster = gbtree # this is the only difference with classification, use reg:linear to do linear classification # when labels are in [0,1] we can also use reg:logistic objective = reg:linear