r descr_models("discrim_linear", "sparsediscrim")
defaults <- tibble::tibble(parsnip = c("regularization_method"), default = c("'diagonal'")) param <- discrim_linear() %>% set_engine("sparsediscrim") %>% make_parameter_list(defaults)
This model has r nrow(param)
tuning parameter:
param$item
The possible values of this parameter, and the functions that they execute, are:
"diagonal"
: [sparsediscrim::lda_diag()]"min_distance"
: [sparsediscrim::lda_emp_bayes_eigen()]"shrink_mean"
: [sparsediscrim::lda_shrink_mean()]"shrink_cov"
: [sparsediscrim::lda_shrink_cov()]r uses_extension("discrim_linear", "sparsediscrim", "classification")
library(discrim) discrim_linear(regularization_method = character(0)) %>% set_engine("sparsediscrim") %>% translate()
lda_diag()
: Dudoit, Fridlyand and Speed (2002) Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data, Journal of the American Statistical Association, 97:457, 77-87.
lda_shrink_mean()
: Tong, Chen, Zhao, Improved mean estimation and its application to diagonal discriminant analysis, Bioinformatics, Volume 28, Issue 4, 15 February 2012, Pages 531-537.
lda_shrink_cov()
: Pang, Tong and Zhao (2009), Shrinkage-based Diagonal Discriminant Analysis and Its Applications in High-Dimensional Data. Biometrics, 65, 1021-1029.
lda_emp_bayes_eigen()
: Srivistava and Kubokawa (2007), Comparison of Discrimination Methods for High Dimensional Data, Journal of the Japan Statistical Society, 37:1, 123-134.
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