Description Usage Arguments Author(s) References Examples
prediction function for tsp.gbm
1 2 |
object |
a tsp.gbm object |
newdata |
new data matrix |
n.trees |
Number of trees used in the prediction. n.trees may be a vector in which case predictions are returned for each iteration specified |
type |
The scale on which gbm makes the predictions |
single.tree |
If single.tree=TRUE then predict.tsp.gbm returns only the predictions from tree(s) n.trees |
... |
not used. |
Xiaolin Yang, Han Liu
gbm package
1 2 3 4 5 |
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-16
Loading required package: tree
Loading required package: randomForest
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
Loading required package: gbm
Loaded gbm 2.1.4
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3858 -nan 0.0010 -0.0001
2 1.3857 -nan 0.0010 -0.0000
3 1.3855 -nan 0.0010 -0.0000
4 1.3853 -nan 0.0010 0.0001
5 1.3852 -nan 0.0010 0.0000
6 1.3850 -nan 0.0010 0.0001
7 1.3849 -nan 0.0010 -0.0002
8 1.3848 -nan 0.0010 -0.0000
9 1.3846 -nan 0.0010 -0.0000
10 1.3844 -nan 0.0010 0.0001
20 1.3827 -nan 0.0010 -0.0001
40 1.3793 -nan 0.0010 -0.0001
60 1.3762 -nan 0.0010 0.0001
80 1.3734 -nan 0.0010 0.0000
100 1.3707 -nan 0.0010 -0.0000
Warning message:
In gbm.fit(newx, y, offset = offset, misc = misc, distribution = distribution, :
Parameter `train.fraction` is deprecated, please specify `nTrain` instead.
[1] -0.04041397 -0.03872335 -0.03734438 -0.04012260 -0.04018410 -0.04035668
[7] -0.04313489 -0.03874362 -0.04054532 -0.03878486
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