Survival Conditional Random Forest Learner
mlr_learners_surv.cforest.Rd
A random forest based on conditional inference trees (ctree).
Calls partykit::cforest()
from partykit.
Parameters
Id | Type | Default | Levels | Range |
ntree | integer | 500 | \([1, \infty)\) | |
replace | logical | FALSE | TRUE, FALSE | - |
fraction | numeric | 0.632 | \([0, 1]\) | |
mtry | integer | - | \([0, \infty)\) | |
mtryratio | numeric | - | \([0, 1]\) | |
applyfun | untyped | - | - | |
cores | integer | NULL | \((-\infty, \infty)\) | |
trace | logical | FALSE | TRUE, FALSE | - |
offset | untyped | - | - | |
cluster | untyped | - | - | |
na.action | untyped | "stats::na.pass" | - | |
scores | untyped | - | - | |
teststat | character | quadratic | quadratic, maximum | - |
splitstat | character | quadratic | quadratic, maximum | - |
splittest | logical | FALSE | TRUE, FALSE | - |
testtype | character | Univariate | Bonferroni, MonteCarlo, Univariate, Teststatistic | - |
nmax | untyped | - | - | |
alpha | numeric | 0.05 | \([0, 1]\) | |
mincriterion | numeric | 0.95 | \([0, 1]\) | |
logmincriterion | numeric | -0.05129329 | \((-\infty, \infty)\) | |
minsplit | integer | 20 | \([1, \infty)\) | |
minbucket | integer | 7 | \([1, \infty)\) | |
minprob | numeric | 0.01 | \([0, 1]\) | |
stump | logical | FALSE | TRUE, FALSE | - |
lookahead | logical | FALSE | TRUE, FALSE | - |
MIA | logical | FALSE | TRUE, FALSE | - |
nresample | integer | 9999 | \([1, \infty)\) | |
tol | numeric | 1.490116e-08 | \([0, \infty)\) | |
maxsurrogate | integer | 0 | \([0, \infty)\) | |
numsurrogate | logical | FALSE | TRUE, FALSE | - |
maxdepth | integer | Inf | \([0, \infty)\) | |
multiway | logical | FALSE | TRUE, FALSE | - |
splittry | integer | 2 | \([0, \infty)\) | |
intersplit | logical | FALSE | TRUE, FALSE | - |
majority | logical | FALSE | TRUE, FALSE | - |
caseweights | logical | TRUE | TRUE, FALSE | - |
saveinfo | logical | FALSE | TRUE, FALSE | - |
update | logical | FALSE | TRUE, FALSE | - |
splitflavour | character | ctree | ctree, exhaustive | - |
maxvar | integer | - | \([1, \infty)\) | |
OOB | logical | FALSE | TRUE, FALSE | - |
simplify | logical | TRUE | TRUE, FALSE | - |
scale | logical | TRUE | TRUE, FALSE | - |
maxpts | integer | 25000 | \((-\infty, \infty)\) | |
abseps | numeric | 0.001 | \([0, \infty)\) | |
releps | numeric | 0 | \([0, \infty)\) |
Custom mlr3 parameters
mtry
:This hyperparameter can alternatively be set via the added hyperparameter
mtryratio
asmtry = max(ceiling(mtryratio * n_features), 1)
. Note thatmtry
andmtryratio
are mutually exclusive.
References
Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.
Hothorn T, Hornik K, Zeileis A (2006). “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics, 15(3), 651–674. doi:10.1198/106186006x133933 , https://doi.org/10.1198/106186006x133933.
See also
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvCForest