chg all settings to obj

demo/binary_classification/mushroom.conf

@@ -2,7 +2,7 @@
 # choose the tree booster, 0: tree, 1: linear
 booster_type = 0 
 # choose logistic regression loss function for binary classification
-loss_type = 2 
+objective = binary:logistic
 
 # Tree Booster Parameters
 # step size shrinkage

demo/kaggle-higgs/higgs-numpy.py

@@ -31,8 +31,9 @@ xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
 
 # setup parameters for xgboost
 param = {}
-# use logistic regression loss
-param['loss_type'] = 3
+# use logistic regression loss, use raw prediction before logistic transformation
+# since we only need the rank
+param['objective'] = 'binary:logitraw'
 # scale weight of positive examples
 param['scale_pos_weight'] = sum_wneg/sum_wpos
 param['bst:eta'] = 0.1 

demo/kaggle-higgs/speedtest.py

@@ -33,7 +33,7 @@ xgmat = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
 # setup parameters for xgboost
 param = {}
 # use logistic regression loss
-param['loss_type'] = 1
+param['objective'] = 'binary:logitraw'
 # scale weight of positive examples
 param['scale_pos_weight'] = sum_wneg/sum_wpos
 param['bst:eta'] = 0.1

demo/rank/runexp.sh

@@ -3,6 +3,5 @@ python trans_data.py test.txt mq2008.test mq2008.test.group
 python trans_data.py vali.txt mq2008.vali mq2008.vali.group
 
 ../../xgboost mq2008.conf
-
-../../xgboost mq2008.conf task=pred model_in=0002.model
+../../xgboost mq2008.conf task=pred model_in=0004.model
 

demo/regression/machine.conf

@@ -1,9 +1,9 @@
 # General Parameters, see comment for each definition
 # choose the tree booster, 0: tree, 1: linear
 booster_type = 0 
-# this is the only difference with classification, use 0: linear regression
-# when labels are in [0,1] we can also use 1: logistic regression
-loss_type = 0
+# 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
 
 # Tree Booster Parameters
 # step size shrinkage