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.
Details
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.
library(censored)
rand_forest() %>%
set_engine("party") %>%
set_mode("censored regression") %>%
translate()## 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().