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

In OHDSI/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

Create setting for gradient boosting machine model using gbm_xgboost implementation

Description

Create setting for gradient boosting machine model using gbm_xgboost implementation

Usage

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))

OHDSI/PatientLevelPrediction documentation built on Sept. 15, 2024, 6:38 a.m.