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Showing content from https://github.com/tidymodels/discrim below:

tidymodels/discrim: Wrappers for discriminant analysis and naive Bayes models for use with the parsnip package

discrim

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::pak("tidymodels/discrim")

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)

parabolic_grid$fda <-
  predict(fda_mod, parabolic_grid, type = "prob")$.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()

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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