In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("discrim_regularized", "klaR")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("frac_identity", "frac_common_cov"),
                 default = c("(see below)", "(see below)"))

param <-
  discrim_regularized() %>% 
  set_engine("klaR") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameter:

param$item

Some special cases for the RDA model:

frac_identity = 0 and frac_common_cov = 1 is a linear discriminant analysis (LDA) model.
frac_identity = 0 and frac_common_cov = 0 is a quadratic discriminant analysis (QDA) model.

Translation from parsnip to the original package

r uses_extension("discrim_regularized", "klaR", "classification")

library(discrim)

discrim_regularized(frac_identity = numeric(0), frac_common_cov = numeric(0)) %>% 
  set_engine("klaR") %>% 
  translate()

Preprocessing requirements

Case weights

References

Friedman, J (1989). Regularized Discriminant Analysis. Journal of the American Statistical Association, 84, 165-175.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.