Create setting for gradient boosting machine model using gbm_xgboost implementation

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))