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add cutomized training

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tqchen 2014-05-04 13:57:10 -07:00
parent 9c2bb12cd1
commit 5fb9376af4
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python/example/demo.py
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@ -86,7 +86,6 @@ bst = xgb.train( param, dtrain, num_round, evallist )
# #
print 'start running example to used cutomized objective function' print 'start running example to used cutomized objective function'
# note: set loss_type properly, loss_type=2 means the prediction will get logistic transformed # 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 # 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 } bst = param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'loss_type':2 }