gpe_rules_pre | R Documentation |
gpe_rules_pre
generates a learner which generates rules like
pre
, which can be supplied to the gpe
base_learner argument.
gpe_rules_pre( learnrate = 0.01, par.init = FALSE, mtry = Inf, maxdepth = 3L, ntrees = 500, tree.control = ctree_control(), use.grad = TRUE, removeduplicates = TRUE, removecomplements = TRUE, tree.unbiased = TRUE )
learnrate |
numeric value > 0. Learning rate or boosting parameter. |
par.init |
logical. Should parallel |
mtry |
positive integer. Number of randomly selected predictor variables for
creating each split in each tree. Ignored when |
maxdepth |
positive integer. Maximum number of conditions in rules.
If |
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 |
use.grad |
logical. Should gradient boosting with regression trees be
employed when |
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 |
## Obtain same fits with pre and gpe set.seed(42) gpe.mod <- gpe(Ozone ~ ., data = airquality[complete.cases(airquality),], base_learners = list(gpe_rules_pre(), gpe_linear())) gpe.mod set.seed(42) pre.mod <- pre(Ozone ~ ., data = airquality[complete.cases(airquality),],) pre.mod
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