Hendrik/xgboost
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change max depth

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This commit is contained in:
tqchen 2014-09-06 22:40:51 -07:00
parent 32a2925be8
commit 89b9965cbf
4 changed files with 5 additions and 5 deletions
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2
R-package/demo/boost_from_prediction.R
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@ -11,7 +11,7 @@ watchlist <- list(eval = dtest, train = dtrain)
#
print('start running example to start from a initial prediction')
# train xgboost for 1 round
param <- list(max_depth=2,eta=1,silent=1,objective='binary:logistic')
param <- list(max.depth=2,eta=1,silent=1,objective='binary:logistic')
bst <- xgb.train( param, dtrain, 1, watchlist )
# Note: we need the margin value instead of transformed prediction in set_base_margin
# do predict with output_margin=TRUE, will always give you margin values before logistic transformation

4
R-package/demo/cross_validation.R
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@ -6,7 +6,7 @@ dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
nround <- 2
param <- list(max_depth=2,eta=1,silent=1,objective='binary:logistic')
param <- list(max.depth=2,eta=1,silent=1,objective='binary:logistic')
cat('running cross validation\n')
# do cross validation, this will print result out as
@ -40,7 +40,7 @@ evalerror <- function(preds, dtrain) {
return(list(metric = "error", value = err))
}
param <- list(max_depth=2,eta=1,silent=1)
param <- list(max.depth=2,eta=1,silent=1)
# train with customized objective
xgb.cv(param, dtrain, nround, nfold = 5,
obj = logregobj, feval=evalerror)

2
R-package/demo/custom_objective.R
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@ -8,7 +8,7 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
# note: for customized objective function, we leave objective as default
# note: what we are getting is margin value in prediction
# you must know what you are doing
param <- list(max_depth=2,eta=1,silent=1)
param <- list(max.depth=2,eta=1,silent=1)
watchlist <- list(eval = dtest, train = dtrain)
num_round <- 2

2
R-package/demo/predict_first_ntree.R
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@ -5,7 +5,7 @@ data(agaricus.test, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
param <- list(max_depth=2,eta=1,silent=1,objective='binary:logistic')
param <- list(max.depth=2,eta=1,silent=1,objective='binary:logistic')
watchlist <- list(eval = dtest, train = dtrain)
nround = 2