`discrim`

contains simple bindings to enable the
`parsnip`

package to fit various discriminant analysis
models, such as

- Linear discriminant analysis (LDA, simple and regularized)
- Quadratic discriminant analysis (QDA, simple and regularized)
- Regularized discriminant analysis (RDA, via Friedman (1989))
- Flexible discriminant analysis (FDA) using MARS features
- Naive Bayes models

You can install the released version of discrim from CRAN with:

`install.packages("discrim")`

And the development version from GitHub with:

```
# install.packages("pak")
::pak("tidymodels/discrim") pak
```

The discrim package provides engines for the models in the following table.

model | engine | mode |
---|---|---|

discrim_flexible | earth | classification |

discrim_linear | MASS | classification |

discrim_linear | mda | classification |

discrim_linear | sda | classification |

discrim_linear | sparsediscrim | classification |

discrim_quad | MASS | classification |

discrim_quad | sparsediscrim | classification |

discrim_regularized | klaR | classification |

naive_Bayes | klaR | classification |

naive_Bayes | naivebayes | classification |

Here is a simple model using a simulated two-class data set contained in the package:

```
library(discrim)
<-
parabolic_grid expand.grid(X1 = seq(-5, 5, length = 100),
X2 = seq(-5, 5, length = 100))
<-
fda_mod discrim_flexible(num_terms = 3) %>%
# increase `num_terms` to find smoother boundaries
set_engine("earth") %>%
fit(class ~ ., data = parabolic)
$fda <-
parabolic_gridpredict(fda_mod, parabolic_grid, type = "prob")$.pred_Class1
library(ggplot2)
ggplot(parabolic, aes(x = X1, y = X2)) +
geom_point(aes(col = class), alpha = .5) +
geom_contour(data = parabolic_grid, aes(z = fda), col = "black", breaks = .5) +
theme_bw() +
theme(legend.position = "top") +
coord_equal()
```

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