SuperLearner (version 2.0-22)

SL.cforest: 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)