knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
discrim
contains simple bindings to enable the parsnip
package to fit various discriminant analysis models, such as
You can install the released version of discrim from CRAN with:
install.packages("discrim")
And the development version from GitHub with:
# install.packages("pak") pak::pak("tidymodels/discrim")
The discrim package provides engines for the models in the following table.
library(parsnip) parsnip_models <- get_from_env("models") %>% setNames(., .) %>% purrr::map_dfr(get_from_env, .id = "model") library(discrim) discrim_models <- get_from_env("models") %>% setNames(., .) %>% purrr::map_dfr(get_from_env, .id = "model") dplyr::anti_join( discrim_models, parsnip_models, by = c("model", "engine", "mode") ) %>% knitr::kable()
Here is a simple model using a simulated two-class data set contained in the package:
#| fig-alt: "Scatter chart. X1 along the x-axis, X2 along the y-axis. points are scattered, with a trend between X1 and X2. Most of the middle points are colored and labeled Class2, with the remaining points labeled Class1. Two connected straight lines, doing its best to separate the two classes." library(discrim) parabolic_grid <- expand.grid(X1 = seq(-5, 5, length = 100), X2 = seq(-5, 5, length = 100)) fda_mod <- discrim_flexible(num_terms = 3) %>% # increase `num_terms` to find smoother boundaries set_engine("earth") %>% fit(class ~ ., data = parabolic) parabolic_grid$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.
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.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.