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.
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.
Case weights
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on tidymodels.org.
The fit() and fit_xy() arguments have arguments called
case_weights that expect vectors of case weights.
Prediction types
parsnip:::get_from_env("boost_tree_predict") |>
dplyr::filter(engine == "mboost") |>
dplyr::select(mode, type)