In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("rule_fit", "h2o")

Tuning Parameters

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".

Translation from parsnip to the underlying model call (regression)

[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()

Translation from parsnip to the underlying model call (classification)

[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()