man/rmd/discrim_quad_sparsediscrim.md

In parsnip: A Common API to Modeling and Analysis Functions

For this engine, there is a single mode: classification

Tuning Parameters

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()]

Translation from parsnip to the original package

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))

Preprocessing requirements

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.

Case weights

The underlying model implementation does not allow for case weights.

Prediction types

parsnip:::get_from_env("discrim_quad_predict") |>
  dplyr::filter(engine == "sparsediscrim") |>
  dplyr::select(mode, type)

## # A tibble: 3 x 2
##   mode           type 
##   <chr>          <chr>
## 1 classification class
## 2 classification prob 
## 3 classification raw

References

• 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.

parsnip documentation built on Jan. 11, 2026, 9:06 a.m.