In parsnip: A Common API to Modeling and Analysis Functions

#| child: aaa.Rmd
#| include: false

r descr_models("rule_fit", "xrf")

Tuning Parameters

#| label: xrf-param-info
#| echo: false
defaults <- 
  tibble::tibble(parsnip = c("tree_depth", "trees", "learn_rate", "mtry",       "min_n", "loss_reduction", "sample_size", "stop_iter", "penalty"),
                 default = c("6L",           "15L",        "0.3",  "see below", "1L",    "0.0",            "1.0",         "Inf",       "0.1"))

param <-
  rule_fit() |> 
  set_engine("xrf") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

#| label: xrf-param-list
#| echo: false
#| results: asis
param$item

Translation from parsnip to the underlying model call (regression)

r uses_extension("rule_fit", "xrf", "regression")

#| label: xrf-reg
library(rules)

rule_fit(
  mtry = numeric(1),
  trees = integer(1),
  min_n = integer(1),
  tree_depth = integer(1),
  learn_rate = numeric(1),
  loss_reduction = numeric(1),
  sample_size = numeric(1),
  penalty = numeric(1)
) |>
  set_engine("xrf") |>
  set_mode("regression") |>
  translate()

Translation from parsnip to the underlying model call (classification)

r uses_extension("rule_fit", "xrf", "classification")

#| label: xrf-cls
library(rules)

rule_fit(
  mtry = numeric(1),
  trees = integer(1),
  min_n = integer(1),
  tree_depth = integer(1),
  learn_rate = numeric(1),
  loss_reduction = numeric(1),
  sample_size = numeric(1),
  penalty = numeric(1)
) |>
  set_engine("xrf") |>
  set_mode("classification") |>
  translate()

Differences from the xrf package

Note that, per the documentation in ?xrf, transformations of the response variable are not supported. To use these with rule_fit(), we recommend using a recipe instead of the formula method.

Also, there are several configuration differences in how xrf() is fit between that package and the wrapper used in rules. Some differences in default values are:

| parameter | xrf | rules | |------------|---------|-----------| | trees | 100 | 15 | |max_depth | 3 | 6 |

These differences will create a disparity in the values of the penalty argument that glmnet uses. Also, rules can also set penalty whereas xrf uses an internal 5-fold cross-validation to determine it (by default).

Preprocessing requirements

#| child: template-makes-dummies.Rmd

Other details

Interpreting mtry

#| child: template-mtry-prop.Rmd

Early stopping

#| child: template-early-stopping.Rmd

Case weights

#| child: template-no-case-weights.Rmd

References

• Friedman and Popescu. "Predictive learning via rule ensembles." Ann. Appl. Stat. 2 (3) 916- 954, September 2008

parsnip documentation built on June 8, 2025, 12:10 p.m.