`MASS::qda()`

fits a model that estimates a multivariate
distribution for the predictors separately for the data in each class
(Gaussian with separate covariance matrices). Bayes' theorem is used
to compute the probability of each class, given the predictor values.

## Details

For this engine, there is a single mode: classification

### Translation from parsnip to the original package

The **discrim** extension package is required to fit this model.

```
library(discrim)
discrim_quad() %>%
set_engine("MASS") %>%
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.