xgboostModel: Run an eXtreme Gradient Boosting model
In ivanliu1989/RQuant: A collection of generic functions

Run an eXtreme Gradient Boosting model

1 2	xgboostModel(datasets, target, paramxgBoost = NULL, nrounds = 100, early.stop.round = 5, print.every.n = 50, verbose = 1)

`datasets`	a list of train, test and validation datasets
`target`	the name of the target feature in datasets
`paramxgBoost`	a list of parameters for xgboost
`nrounds`	the max number of iterations
`early.stop.round`	If NULL, the early stopping function is not triggered. If set to an integer k, training with a validation set will stop if the performance keeps getting worse consecutively for k rounds.
`print.every.n`	Print every N progress messages when verbose>0. Default is 1 which means all messages are printed.
`verbose`	If 0, xgboost will stay silent. If 1, xgboost will print information of performance. If 2, xgboost will print information of both

set.seed(42)
x = iris
x$Species = ifelse(x$Species == "versicolor",1, 0)
dat = splitDataToTrainTestDataFrame(x,.9,.25)
mod = xgboostModel(dat,"Species",nrounds = 200, early.stop.round = 5, verbose = 1)