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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

Installation

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")

Available Engines

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

Example

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()

Scatter chart. X1 along the x-axis, X2 along the y-axis. points are scattered, with a trend between X1 and X2. Most of the middle points are colored and labeled Class2, with the remaining points labeled Class1. Two connected straight lines, doing its best to separate the two classes.

Contributing

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

For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
If you think you have encountered a bug, please submit an issue.
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
Check out further details on contributing guidelines for tidymodels packages and how to get help.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.