Create setting for gradient boosting machine model using gbm_xgboost implementation

```
setGradientBoostingMachine(
ntrees = c(100, 300),
nthread = 20,
earlyStopRound = 25,
maxDepth = c(4, 6, 8),
minChildWeight = 1,
learnRate = c(0.05, 0.1, 0.3),
scalePosWeight = 1,
lambda = 1,
alpha = 0,
seed = sample(1e+07, 1)
)
```

## Arguments

- ntrees
The number of trees to build

- nthread
The number of computer threads to use (how many cores do you have?)

- earlyStopRound
If the performance does not increase over earlyStopRound number of trees then training stops (this prevents overfitting)

- maxDepth
Maximum depth of each tree - a large value will lead to slow model training

- minChildWeight
Minimum sum of of instance weight in a child node - larger values are more conservative

- learnRate
The boosting learn rate

- scalePosWeight
Controls weight of positive class in loss - useful for imbalanced classes

- lambda
L2 regularization on weights - larger is more conservative

- alpha
L1 regularization on weights - larger is more conservative

- seed
An option to add a seed when training the final model

## Examples

```
model.gbm <- setGradientBoostingMachine(ntrees=c(10,100), nthread=20,
maxDepth=c(4,6), learnRate=c(0.1,0.3))
```