`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()`

.