In topepo/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("rand_forest", "partykit")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("trees", "min_n", "mtry"),
                 default = c("500L", "20L", "5L"))

param <-
  rand_forest() %>% 
  set_engine("partykit") %>% 
  set_mode("regression") %>%  
  make_parameter_list(defaults) %>% 
  distinct()

This model has r nrow(param) tuning parameters:

param$item

Translation from parsnip to the original package (regression)

r uses_extension("rand_forest", "partykit", "regression")

library(bonsai)

rand_forest() %>% 
  set_engine("partykit") %>% 
  set_mode("regression") %>% 
  translate()

Translation from parsnip to the original package (classification)

r uses_extension("rand_forest", "partykit", "classification")

library(bonsai)

rand_forest() %>% 
  set_engine("partykit") %>% 
  set_mode("classification") %>% 
  translate()

parsnip::cforest_train() is a wrapper around [partykit::cforest()] (and other functions) that makes it easier to run this model.

Translation from parsnip to the original package (censored regression)

r uses_extension("rand_forest", "partykit", "censored regression")

library(censored)

rand_forest() %>% 
  set_engine("partykit") %>% 
  set_mode("censored regression") %>% 
  translate()

censored::cond_inference_surv_cforest() is a wrapper around [partykit::cforest()] (and other functions) that makes it easier to run this model.

Preprocessing requirements

Other details

References

• partykit: A Modular Toolkit for Recursive Partytioning in R

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

topepo/parsnip documentation built on April 16, 2024, 3:23 a.m.