Create setting for gradient boosting machine model using gbm_xgboost implementation

setGradientBoostingMachine(ntrees = c(100, 1000), nthread = 20,
earlyStopRound = 25, maxDepth = c(4, 6, 17), minRows = 2,
learnRate = c(0.005, 0.01, 0.1), seed = NULL)

## Arguments

ntrees The number of trees to build The number of computer threads to (how many cores do you have?) If the performance does not increase over earlyStopRound number of interactions then training stops (this prevents overfitting) Maximum number of interactions - a large value will lead to slow model training The minimum number of rows required at each end node of the tree The boosting learn rate 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))