in traditional gbm, we can use
 predict.gbm(model, newsdata=..., n.tree=...)

So that I can compare result with different number of trees for the test data.

In h2o.gbm, although it has n.tree to set, it seems it doesn't have any effect on the result. It's all the same as the default model:

```
h2o.test.pred <- as.vector(h2o.predict(h2o.gbm.model, newdata=test.frame, n.tree=100))
R2(h2o.test.pred, test.mat$y)
[1] -0.00714109
h2o.test.pred <- as.vector(h2o.predict(h2o.gbm.model, newdata=test.frame, n.tree=10))
> R2(h2o.test.pred, test.mat$y)
[1] -0.00714109

```

Does anybod have similar problem? How to solve it? h2o.gbm is much faster than gbm, so if it can get detailed result of each tree that would be great.