r descr_models("boost_tree", "mboost")
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:
param$item
The mtry
parameter is related to the number of predictors. The default is to use all predictors.
r uses_extension("boost_tree", "mboost", "censored regression")
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.
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.
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