From 5fb9376af450a87f11d89d4a1f23c0c2be653c51 Mon Sep 17 00:00:00 2001 From: tqchen Date: Sun, 4 May 2014 13:57:10 -0700 Subject: [PATCH] add cutomized training --- python/example/demo.py | 1 - 1 file changed, 1 deletion(-) diff --git a/python/example/demo.py b/python/example/demo.py index b3f091f62..b6a21731b 100755 --- a/python/example/demo.py +++ b/python/example/demo.py @@ -86,7 +86,6 @@ bst = xgb.train( param, dtrain, num_round, evallist ) # print 'start running example to used cutomized objective function' - # note: set loss_type properly, loss_type=2 means the prediction will get logistic transformed # in most case, we may want to set loss_type = 0, to get untransformed score to compute gradient bst = param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'loss_type':2 }