setGradientBoostingMachine: Create setting for gradient boosting machine model using...

In hxia/plp-git-demo: Package for patient level prediction using data in the OMOP Common Data Model

Description

Create setting for gradient boosting machine model using gbm_xgboost implementation

Usage

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

hxia/plp-git-demo documentation built on March 19, 2021, 1:54 a.m.