README.md

In tidymodels/discrim: Model Wrappers for Discriminant Analysis

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()

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.

tidymodels/discrim documentation built on April 15, 2024, 9:19 p.m.