`partykit::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 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()
```

### 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.