xgboostxgboost_wrapper.RdCompatible learner wrappers for this package should have a specific format.
Namely they should take as input a list called train that contains
named objects $Y and $X, that contain, respectively, the outcomes
and predictors in a particular training fold. Other options may be passed in
to the function as well. The function must output a list with the following
named objects: test_pred = predictions of test$Y based on the learner
fit using train$X; train_pred = prediction of train$Y based
on the learner fit using train$X; model = the fitted model (only
necessary if you desire to look at this model later, not used for internal
computations); train_y = a copy of train$Y; test_y = a copy
of test$Y.
xgboost_wrapper( test, train, ntrees = 500, max_depth = 4, shrinkage = 0.1, minobspernode = 2, params = list(), nthread = 1, verbose = 0, save_period = NULL )
| test | A list with named objects |
|---|---|
| train | A list with named objects |
| ntrees | See xgboost |
| max_depth | See xgboost |
| shrinkage | See xgboost |
| minobspernode | See xgboost |
| params | See xgboost |
| nthread | See xgboost |
| verbose | See xgboost |
| save_period | See xgboost |
A list with named objects (see description).
This particular wrapper implements eXtreme gradient boosting using xgboost. We refer readers to the original package's documentation for more details.
# simulate data # make list of training data train_X <- data.frame(x1 = runif(50)) train_Y <- rbinom(50, 1, plogis(train_X$x1)) train <- list(Y = train_Y, X = train_X) # make list of test data test_X <- data.frame(x1 = runif(50)) test_Y <- rbinom(50, 1, plogis(train_X$x1)) test <- list(Y = test_Y, X = test_X) # fit xgboost xgb_wrap <- xgboost_wrapper(train = train, test = test)