Skip to content

party::cforest() fits a model that creates a large number of decision trees, each independent of the others. The final prediction uses all predictions from the individual trees and combines them.


For this engine, there is a single mode: censored regression

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 (censored regression)

The censored extension package is required to fit this model.


rand_forest() %>% 
  set_engine("party") %>% 
  set_mode("censored regression") %>% 

## Random Forest Model Specification (censored regression)
## Computational engine: party 
## Model fit template:
## censored::cond_inference_surv_cforest(formula = missing_arg(), 
##     data = missing_arg())

censored::cond_inference_surv_cforest() is a wrapper around party::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

The main interface for this model uses the formula method since the model specification typically involved the use of survival::Surv().


  • Hothorn T, Buhlmann P, Dudoit S, Molinaro A, Van der Laan MJ. 2006. Survival Ensembles. Biostatistics, 7(3), 355–373.

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