parsnip: A Common API to Modeling and Analysis Functions

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(), 
##     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.