randomforest_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$Y.
randomforest_wrapper( train, test, mtry = floor(sqrt(ncol(train$X))), ntree = 1000, nodesize = 1, maxnodes = NULL, importance = FALSE, ... )
| train | A list with named objects |
|---|---|
| test | A list with named objects |
| mtry | See randomForest. |
| ntree | See randomForest. |
| nodesize | See randomForest. |
| maxnodes | See randomForest. |
| importance | See randomForest. |
| ... | Other options (passed to |
A list with named objects (see description).
This particular wrapper implements the randomForest ensemble methodology. 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 randomforest rf_wrap <- randomforest_wrapper(train = train, test = test)