my.xgb.cv: Title Cross-validation for evaluating the performance of...

In liupeng2117/SubgroupBoost: Value function Guided Subgrouping via Gradient Tree Boosting

Description

Title Cross-validation for evaluating the performance of subgrouping using win-ratio based value function

Usage

my.xgb.cv(
  params,
  data,
  label,
  comp.ind,
  nrounds,
  nfold,
  maximize,
  verbose,
  early_stopping_rounds
)

Arguments

params	the list of parameters for xgboost
data	the data of predictors
label	treatment label of subjects
comp.ind	The vector of comparision results
nrounds	the maximum number of boosting rounds.
nfold	number of folds in cross validation
maximize	a logical value, if TRUE, maximize the loss function, here we set it to FALSE
verbose	a logical value, whether print out the messages of fitting the model
early_stopping_rounds	the number of rounds before stopping if no improvement, defualt is 5

Value

a vector of errors

Examples

NULL

liupeng2117/SubgroupBoost documentation built on Feb. 7, 2022, 1 p.m.