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Merge branch 'master' of ssh://github.com/dmlc/xgboost

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tqchen 2015-07-29 18:24:27 -07:00
commit 26675e6dcd
2 changed files with 9 additions and 4 deletions
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5
.travis.yml
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@ -1,5 +1,10 @@
sudo: true sudo: true
# Enabling test on Linux and OS X
os:
- linux
- osx
# Use Build Matrix to do lint and build seperately # Use Build Matrix to do lint and build seperately
env: env:
matrix: matrix:

8
R-package/demo/custom_objective.R
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@ -33,7 +33,7 @@ evalerror <- function(preds, dtrain) {
return(list(metric = "error", value = err)) return(list(metric = "error", value = err))
} }
param <- list(max.depth=2,eta=1,nthread = 2, silent=1, param <- list(max.depth=2, eta=1, nthread = 2, silent=1,
objective=logregobj, eval_metric=evalerror) objective=logregobj, eval_metric=evalerror)
print ('start training with user customized objective') print ('start training with user customized objective')
# training with customized objective, we can also do step by step training # training with customized objective, we can also do step by step training
@ -57,9 +57,9 @@ logregobjattr <- function(preds, dtrain) {
hess <- preds * (1 - preds) hess <- preds * (1 - preds)
return(list(grad = grad, hess = hess)) return(list(grad = grad, hess = hess))
} }
param <- list(max.depth=2, eta=1, nthread = 2, silent=1,
objective=logregobjattr, eval_metric=evalerror)
print ('start training with user customized objective, with additional attributes in DMatrix') print ('start training with user customized objective, with additional attributes in DMatrix')
# training with customized objective, we can also do step by step training # training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train # simply look at xgboost.py's implementation of train
bst <- xgb.train(param, dtrain, num_round, watchlist, bst <- xgb.train(param, dtrain, num_round, watchlist)
objective=logregobj, eval_metric=evalerror)