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# Create some output to test the new print function and
# to be comparable with any future changes to gbm.
library( MASS )
data( birthwt )
data( VA )
data( iris )
data( fgl )
data( cpus )
data( housing )
set.seed( 20090417 )
bMod <- gbm( low ~ ., data=birthwt,
n.tree=1000, shrinkage=.01, cv.folds=5,
verbose = FALSE, n.cores=1)
bMod
bwt <- birthwt
bwt <- bwt[ sample( 1:nrow( bwt ) ),]
aMod <- gbm( low ~ ., data=bwt, distribution="adaboost",
n.trees=1000, shrinkage=.01, cv.folds=10,
train.fraction=.9, verbose = FALSE , n.cores=1)
aMod
cMod <- gbm( Surv( stime, status ) ~ treat + age + Karn + diag.time + cell + prior,
data = VA, n.tree = 1000, shrinkage=.1, cv.folds = 5,
verbose = FALSE, n.cores=1)
cMod
kMod <- gbm( Species ~ . , data=iris , n.tree=1000, shrinkage=.1,
cv.folds=5, train.fraction=.9, n.cores=1 )
kMod
kMod2 <- gbm( type ~ ., data=fgl, n.tree=1000, shrinkage=.01,
cv.folds=5, n.cores=1 )
kMod2
mycpus <- cpus
mycpus <- mycpus[, -1 ]
gMod <- gbm( log( perf ) ~ ., data = mycpus, distribution="gaussian",
cv.folds=5, n.trees=1000, shrinkage=.01,
verbose = FALSE, n.cores=1)
gMod
biMod <- gbm( log(perf) ~ ., data=mycpus,
cv.folds=5, n.trees=1000, shrinkage=.01, n.cores=1 )
biMod
tMod <- gbm( log(perf) ~ ., data=mycpus, distribution="tdist",
cv.folds=5, n.trees=1000, shrinkage=.01,
interaction.depth= 3, n.cores=1)
tMod
lMod <- gbm( log(perf) ~ ., data=mycpus, distribution="laplace",
cv.folds=5, n.trees=1000, shrinkage=.01,
interaction.depth= 3, n.cores=1)
lMod
qMod <- gbm( log(perf) ~ ., data=mycpus,
distribution=list(name="quantile", alpha=.7 ),
cv.folds=5, n.trees=1000, shrinkage=.01,
interaction.depth= 3, verbose = FALSE, n.cores=1)
qMod
pMod <- gbm( Freq ~ ., data=housing , distribution="poisson",
n.trees=1000, cv.folds=5 , shrinkage=.01,
interaction.depth = 3, n.cores=1)
pMod
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