In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("boost_tree", "mboost")

Tuning Parameters

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defaults <- 
  tibble::tibble(parsnip = c("mtry", "trees", "tree_depth", "min_n", "loss_reduction"),
                 default = c("see below", "100L", "2L", "10L", "0"))

param <-
  boost_tree() |> 
  set_engine("mboost") |> 
  set_mode("censored regression") |>
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

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param$item

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)

r uses_extension("boost_tree", "mboost", "censored regression")

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library(censored)

boost_tree() |> 
  set_engine("mboost") |> 
  set_mode("censored regression") |> 
  translate()

censored::blackboost_train() is a wrapper around [mboost::blackboost()] (and other functions) that makes it easier to run this model.

Preprocessing requirements

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Other details

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References

Buehlmann P, Hothorn T. 2007. Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.