For this engine, there is a single mode: classification
This model has 1 tuning parameter:
regularization_method
: Regularization Method (type: character, default: 'diagonal')The possible values of this parameter, and the functions that they execute, are:
"diagonal"
: [sparsediscrim::qda_diag()]"shrink_mean"
: [sparsediscrim::qda_shrink_mean()]"shrink_cov"
: [sparsediscrim::qda_shrink_cov()]The discrim extension package is required to fit this model.
library(discrim)
discrim_quad(regularization_method = character(0)) %>%
set_engine("sparsediscrim") %>%
translate()
## Quadratic Discriminant Model Specification (classification)
##
## Main Arguments:
## regularization_method = character(0)
##
## Computational engine: sparsediscrim
##
## Model fit template:
## discrim::fit_regularized_quad(x = missing_arg(), y = missing_arg(),
## regularization_method = character(0))
Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
Variance calculations are used in these computations within each outcome class. For this reason, zero-variance predictors (i.e., with a single unique value) within each class should be eliminated before fitting the model.
The underlying model implementation does not allow for case weights.
qda_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.
qda_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.
qda_shrink_cov()
: Pang, Tong and Zhao (2009), Shrinkage-based Diagonal Discriminant Analysis and Its Applications in High-Dimensional Data. Biometrics, 65, 1021-1029.
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