man/rmd/rand_forest_partykit.md

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

For this engine, there are multiple modes: censored regression, regression, and classification

Tuning Parameters

This model has 3 tuning parameters:

• trees: # Trees (type: integer, default: 500L)

• min_n: Minimal Node Size (type: integer, default: 20L)

• mtry: # Randomly Selected Predictors (type: integer, default: 5L)

Translation from parsnip to the original package (regression)

The bonsai extension package is required to fit this model.

library(bonsai)

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

## Random Forest Model Specification (regression)
## 
## Computational engine: partykit 
## 
## Model fit template:
## parsnip::cforest_train(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg())

Translation from parsnip to the original package (classification)

The bonsai extension package is required to fit this model.

library(bonsai)

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

## Random Forest Model Specification (classification)
## 
## Computational engine: partykit 
## 
## Model fit template:
## parsnip::cforest_train(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg())

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)

The censored extension package is required to fit this model.

library(censored)

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

## Random Forest Model Specification (censored regression)
## 
## Computational engine: partykit 
## 
## Model fit template:
## parsnip::cforest_train(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg())

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

Preprocessing requirements

This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. {a, c} vs {b, d}) when splitting at a node. Dummy variables are not required for this model.

Other details

Predictions of type "time" are predictions of the median survival time.

References

• partykit: A Modular Toolkit for Recursive Partytioning in R

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

tidymodels/parsnip documentation built on Feb. 19, 2025, 2:10 a.m.