SL.cforest: cforest party

In SuperLearner: Super Learner Prediction

cforest party

Description

These defaults emulate cforest_unbiased() but allow customization.

Usage

SL.cforest(Y, X, newX, family, obsWeights, id, ntree = 1000,
  mtry = max(floor(ncol(X)/3), 1), mincriterion = 0, teststat = "quad",
  testtype = "Univ", replace = F, fraction = 0.632, ...)

Arguments

Y	Outcome variable
X	Covariate dataframe
newX	Optional dataframe to predict the outcome
family	"gaussian" for regression, "binomial" for binary classification
obsWeights	Optional observation-level weights (supported but not tested)
id	Optional id to group observations from the same unit (not used currently).
ntree	Number of trees
mtry	Number of randomly selected features per node
mincriterion	See ?cforest_control
teststat	See ?cforest_control
testtype	See ?cforest_control
replace	See ?cforest_control
fraction	See ?cforest_control
...	Remaining arguments (unused)

SuperLearner documentation built on July 26, 2023, 6:05 p.m.