`mboost::blackboost()`

fits a series of decision trees forming an ensemble.
Each tree depends on the results of previous trees. All trees in the
ensemble are combined to produce a final prediction.

## Details

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

### Tuning Parameters

This model has 5 tuning parameters:

`mtry`

: # Randomly Selected Predictors (type: integer, default: see below)`trees`

: # Trees (type: integer, default: 100L)`tree_depth`

: Tree Depth (type: integer, default: 2L)`min_n`

: Minimal Node Size (type: integer, default: 10L)`loss_reduction`

: Minimum Loss Reduction (type: double, default: 0)

The `mtry`

parameter is related to the number of predictors. The default
is to use all predictors.

### Translation from parsnip to the original package (censored regression)

The **censored** extension package is required to fit this model.

```
library(censored)
boost_tree() %>%
set_engine("mboost") %>%
set_mode("censored regression") %>%
translate()
```

```
## Boosted Tree Model Specification (censored regression)
##
## Computational engine: mboost
##
## Model fit template:
## censored::blackboost_train(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg(), family = mboost::CoxPH())
```

`censored::blackboost_train()`

is a wrapper around
`mboost::blackboost()`

(and other functions)
that makes it easier to run this model.