r descr_models("boost_tree", "spark")
. However, multiclass classification is not supported yet.
defaults <- tibble::tibble(parsnip = c("tree_depth", "trees", "learn_rate", "mtry", "min_n", "loss_reduction", "sample_size"), default = c("5L", "20L", "0.1", "see below", "1L", "0.0", "1.0")) # For this model, this is the same for all modes param <- boost_tree() %>% set_engine("spark") %>% set_mode("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 depends on the model mode. For classification, the square root of the number of predictors is used and for regression, one third of the predictors are sampled.
boost_tree( mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(), learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric() ) %>% set_engine("spark") %>% set_mode("regression") %>% translate()
boost_tree( mtry = integer(), trees = integer(), min_n = integer(), tree_depth = integer(), learn_rate = numeric(), loss_reduction = numeric(), sample_size = numeric() ) %>% set_engine("spark") %>% set_mode("classification") %>% translate()
Note that, for spark engines, the case_weight
argument value should be a character string to specify the column with the numeric case weights.
Luraschi, J, K Kuo, and E Ruiz. 2019. Mastering Spark with R. O'Reilly Media
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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