r descr_models("rule_fit", "h2o")
defaults <- tibble::tibble(parsnip = c("tree_depth", "trees", "penalty"), default = c("3L", "50L", 0)) param <- rule_fit() %>% set_engine("h2o") %>% make_parameter_list(defaults)
This model has r nrow(param)
tuning parameters:
param$item
Note that penalty
for the h2o engine in `rule_fit()`` corresponds to the L1 penalty (LASSO).
Other engine arguments of interest:
algorithm
: The algorithm to use to generate rules. should be one of "AUTO", "DRF", "GBM", defaults to "AUTO".
min_rule_length
: Minimum length of tree depth, opposite of tree_dpeth
, defaults to 3.
max_num_rules
: The maximum number of rules to return. The default value of -1 means the number of rules is selected by diminishing returns in model deviance.
model_type
: The type of base learners in the ensemble, should be one of: "rules_and_linear", "rules", "linear", defaults to "rules_and_linear".
[agua::h2o_train_rule()] is a wrapper around [h2o::h2o.rulefit()].
r uses_extension("rule_fit", "h2o", "regression")
library(rules) rule_fit( trees = integer(1), tree_depth = integer(1), penalty = numeric(1) ) %>% set_engine("h2o") %>% set_mode("regression") %>% translate()
[agua::h2o_train_rule()] for rule_fit()
is a wrapper around [h2o::h2o.rulefit()].
r uses_extension("rule_fit", "h2o", "classification")
rule_fit( trees = integer(1), tree_depth = integer(1), penalty = numeric(1) ) %>% set_engine("h2o") %>% set_mode("classification") %>% translate()
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