- learnrate
numeric value \(> 0\). Learning rate or boosting parameter.
- par.init
logical. Should parallel foreach
be used to generate
initial ensemble? Only used when learnrate == 0
. Note: Must register
parallel beforehand, such as doMC or others. Furthermore, setting
par.init = TRUE
will likely only increase computation time for smaller
datasets.
- mtry
positive integer. Number of randomly selected predictor variables for
creating each split in each tree. Ignored when tree.unbiased=FALSE
.
- maxdepth
positive integer. Maximum number of conditions in rules.
If length(maxdepth) == 1
, it specifies the maximum depth of
of each tree grown. If length(maxdepth) == ntrees
, it specifies the
maximum depth of every consecutive tree grown. Alternatively, a random
sampling function may be supplied, which takes argument ntrees
and
returns integer values. See also maxdepth_sampler
.
- ntrees
positive integer value. Number of trees to generate for the
initial ensemble.
- tree.control
list with control parameters to be passed to the tree
fitting function, generated using ctree_control
,
mob_control
(if use.grad = FALSE
), or
rpart.control
(if tree.unbiased = FALSE
).
- use.grad
logical. Should gradient boosting with regression trees be
employed when learnrate > 0
? If TRUE
, use trees fitted by
ctree
or rpart
as in Friedman
(2001), but without the line search. If use.grad = FALSE
,
glmtree
instead of ctree
will be employed for rule induction, yielding longer computation times,
higher complexity, but possibly higher predictive accuracy. See Details for
supported combinations of family
, use.grad
and learnrate
.
- removeduplicates
logical. Remove rules from the ensemble which are
identical to an earlier rule?
- removecomplements
logical. Remove rules from the ensemble which are
identical to (1 - an earlier rule)?
- tree.unbiased
logical. Should an unbiased tree generation algorithm
be employed for rule generation? Defaults to TRUE
, if set to
FALSE
, rules will be generated employing the CART algorithm
(which suffers from biased variable selection) as implemented in
rpart
. See details below for possible combinations
with family
, use.grad
and learnrate
.