In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("boost_tree", "spark"). However, multiclass classification is not supported yet.

Tuning Parameters

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.

Translation from parsnip to the original package (regression)

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()

Translation from parsnip to the original package (classification)

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()

Preprocessing requirements

Case weights

Note that, for spark engines, the case_weight argument value should be a character string to specify the column with the numeric case weights.

Other details

References

• 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.

parsnip documentation built on Aug. 18, 2023, 1:07 a.m.