# Quadratic discriminant analysis via regularization

Source:`R/discrim_quad_sparsediscrim.R`

`details_discrim_quad_sparsediscrim.Rd`

Functions in the sparsediscrim package fit different types of quadratic discriminant analysis model that regularize the estimates (like the mean or covariance).

## Details

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()
```

### 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 `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.

### 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.